{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b2b5ec3f-2b03-4e2c-8d2b-c49320122f1f",
   "metadata": {},
   "source": [
    "## Code for Analyazing Topic Changes over Time in Reddit Data\n",
    "\n",
    "*The following notebook contains code that was used in the analysis of topic changes over time for Reddit Data.* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b6859283",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "from collections import Counter\n",
    "import json\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.cluster import hierarchy\n",
    "from scipy.spatial.distance import squareform\n",
    "from matplotlib.colors import Normalize\n",
    "from matplotlib.cm import ScalarMappable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01004ef5-f089-4494-bf49-06a6fa1bd111",
   "metadata": {},
   "source": [
    "### Filter Temporal Datasets Based on Corpus Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c31f5c93-2814-4d09-a69a-d4e3bffa0779",
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_df = pd.read_csv('Data/show_topic_averages_before.csv', index_col=0)\n",
    "post_df = pd.read_csv('Data/show_topic_averages_after.csv', index_col=0)\n",
    "\n",
    "pre_filter_list = ['Bridgerton', 'Euphoria', 'Hawkeye', 'Moon Knight', 'PAW Patrol', 'The Big Bang Theory', 'The Book of Boba Fett', 'The Wheel of Time']\n",
    "post_filter_list = ['13 Reasons Why', 'American Horror Story', 'Arrow', 'Black Mirror', 'Brooklyn Nine-Nine', 'Dark', \"Grey's Anatomy\", 'Jujutsu Kaisen',\n",
    "                    'La Casa De Papel', 'Loki', 'Lucifer', \"Marvel's Agents of SHIELD\", 'My Hero Academia', 'Orange is the New Black', 'Outlander',\n",
    "                    'PAW Patrol', 'Riverdale', 'South Park', 'SpongeBob SquarePants', 'Supernatural', 'The 100', 'The Big Bang Theory', \n",
    "                    'The Falcon and the Winter Soldier', 'The Flash', 'The Walking Dead', 'Vikings', 'WandaVision', 'Westworld']\n",
    "\n",
    "#filter out shows with < 10K comments in each period\n",
    "pre_df = pre_df[~pre_df.index.isin(pre_filter_list)]\n",
    "post_df = post_df[~post_df.index.isin(post_filter_list)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93443460-4ca7-45e1-a0b7-88691082cad0",
   "metadata": {},
   "source": [
    "### Before/After Squid Game Analysis -- Align topics in the before and after Squid Game topic models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "928bbd30-77ed-4972-8bf8-e2df478cd0df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic alignment analysis code ready!\n",
      "\n",
      "Usage:\n",
      "1. Load your topic words data:\n",
      "   pre_words = pd.read_csv('pre_topic_words.csv', index_col=0)\n",
      "   post_words = pd.read_csv('post_topic_words.csv', index_col=0)\n",
      "2. Load topic metrics:\n",
      "   pre_metrics = load_topic_metrics('pre_topics.xml')\n",
      "   post_metrics = load_topic_metrics('post_topics.xml')\n",
      "3. Run alignment analysis:\n",
      "   results = run_topic_alignment_analysis(pre_words, post_words, pre_metrics, post_metrics)\n",
      "4. Examine specific alignments:\n",
      "   examine_alignment(pre_words, post_words, 5, 12)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from scipy.spatial.distance import jensenshannon\n",
    "import xml.etree.ElementTree as ET\n",
    "import re\n",
    "from collections import defaultdict\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "def load_topic_words(filepath_or_data, format_type='csv'):\n",
    "    \"\"\"\n",
    "    Load topic word distributions from file.\n",
    "    \n",
    "    Parameters:\n",
    "    - filepath_or_data: Path to file or DataFrame with topic words\n",
    "    - format_type: 'csv', 'txt', or 'dataframe'\n",
    "    \n",
    "    Returns:\n",
    "    - DataFrame with topics as rows, words as columns (or vice versa)\n",
    "    \"\"\"\n",
    "    \n",
    "    if format_type == 'dataframe':\n",
    "        return filepath_or_data\n",
    "    elif format_type == 'csv':\n",
    "        return pd.read_csv(filepath_or_data, index_col=0)\n",
    "    elif format_type == 'txt':\n",
    "        # Assuming format: topic_id: word1, word2, word3, ...\n",
    "        topic_words = {}\n",
    "        with open(filepath_or_data, 'r') as f:\n",
    "            for line in f:\n",
    "                if ':' in line:\n",
    "                    topic_id, words = line.strip().split(':', 1)\n",
    "                    topic_words[topic_id.strip()] = [w.strip() for w in words.split(',')]\n",
    "        return pd.DataFrame.from_dict(topic_words, orient='index')\n",
    "    \n",
    "def load_topic_metrics(xml_filepath):\n",
    "    \"\"\"\n",
    "    Load topic quality metrics from XML file.\n",
    "    \n",
    "    Returns:\n",
    "    - DataFrame with topic metrics\n",
    "    \"\"\"\n",
    "    \n",
    "    tree = ET.parse(xml_filepath)\n",
    "    root = tree.getroot()\n",
    "    \n",
    "    metrics_data = []\n",
    "    for topic in root.findall('topic'):\n",
    "        topic_metrics = {'topic_id': int(topic.get('id'))}\n",
    "        \n",
    "        # Extract all attributes\n",
    "        for attr, value in topic.attrib.items():\n",
    "            if attr != 'id':\n",
    "                try:\n",
    "                    topic_metrics[attr] = float(value)\n",
    "                except ValueError:\n",
    "                    topic_metrics[attr] = value\n",
    "        \n",
    "        metrics_data.append(topic_metrics)\n",
    "    \n",
    "    return pd.DataFrame(metrics_data).set_index('topic_id')\n",
    "\n",
    "def calculate_topic_similarity_matrix(pre_words, post_words, method='jaccard'):\n",
    "    \"\"\"\n",
    "    Calculate similarity matrix between pre and post topics based on word overlap.\n",
    "    \n",
    "    Parameters:\n",
    "    - pre_words: DataFrame of pre-period topic words\n",
    "    - post_words: DataFrame of post-period topic words  \n",
    "    - method: 'jaccard', 'cosine', 'tfidf_cosine'\n",
    "    \n",
    "    Returns:\n",
    "    - similarity_matrix: Matrix of similarities (pre_topics x post_topics)\n",
    "    \"\"\"\n",
    "    \n",
    "    n_pre = len(pre_words)\n",
    "    n_post = len(post_words)\n",
    "    similarity_matrix = np.zeros((n_pre, n_post))\n",
    "    \n",
    "    if method == 'jaccard':\n",
    "        # Jaccard similarity of top words\n",
    "        for i, (pre_idx, pre_row) in enumerate(pre_words.iterrows()):\n",
    "            pre_words_set = set([w for w in pre_row.dropna() if pd.notna(w)])\n",
    "            \n",
    "            for j, (post_idx, post_row) in enumerate(post_words.iterrows()):\n",
    "                post_words_set = set([w for w in post_row.dropna() if pd.notna(w)])\n",
    "                \n",
    "                if len(pre_words_set) > 0 and len(post_words_set) > 0:\n",
    "                    intersection = len(pre_words_set & post_words_set)\n",
    "                    union = len(pre_words_set | post_words_set)\n",
    "                    similarity_matrix[i, j] = intersection / union if union > 0 else 0\n",
    "    \n",
    "    elif method == 'cosine' or method == 'tfidf_cosine':\n",
    "        # Convert topic words to text strings\n",
    "        pre_texts = []\n",
    "        post_texts = []\n",
    "        \n",
    "        for _, row in pre_words.iterrows():\n",
    "            words = [w for w in row.dropna() if pd.notna(w)]\n",
    "            pre_texts.append(' '.join(words))\n",
    "            \n",
    "        for _, row in post_words.iterrows():\n",
    "            words = [w for w in row.dropna() if pd.notna(w)]\n",
    "            post_texts.append(' '.join(words))\n",
    "        \n",
    "        # Create TF-IDF vectors\n",
    "        all_texts = pre_texts + post_texts\n",
    "        \n",
    "        if method == 'tfidf_cosine':\n",
    "            vectorizer = TfidfVectorizer(lowercase=True, token_pattern=r'\\b\\w+\\b')\n",
    "        else:\n",
    "            # Simple word count vectors\n",
    "            vectorizer = TfidfVectorizer(lowercase=True, token_pattern=r'\\b\\w+\\b', use_idf=False)\n",
    "        \n",
    "        try:\n",
    "            vectors = vectorizer.fit_transform(all_texts)\n",
    "            pre_vectors = vectors[:n_pre]\n",
    "            post_vectors = vectors[n_pre:]\n",
    "            \n",
    "            # Calculate cosine similarity\n",
    "            similarity_matrix = cosine_similarity(pre_vectors, post_vectors)\n",
    "        except ValueError as e:\n",
    "            print(f\"Error in vectorization: {e}\")\n",
    "            print(\"Falling back to Jaccard similarity\")\n",
    "            return calculate_topic_similarity_matrix(pre_words, post_words, method='jaccard')\n",
    "    \n",
    "    return similarity_matrix\n",
    "\n",
    "def find_best_alignments(similarity_matrix, pre_topics, post_topics, \n",
    "                        threshold=0.15, max_matches_per_topic=3):\n",
    "    \"\"\"\n",
    "    Find best topic alignments based on similarity matrix.\n",
    "    \n",
    "    Parameters:\n",
    "    - similarity_matrix: Matrix of topic similarities\n",
    "    - pre_topics: List of pre-period topic IDs\n",
    "    - post_topics: List of post-period topic IDs\n",
    "    - threshold: Minimum similarity to consider a match\n",
    "    - max_matches_per_topic: Maximum number of matches per topic\n",
    "    \n",
    "    Returns:\n",
    "    - alignments: List of (pre_topic, post_topic, similarity) tuples\n",
    "    - unmatched_pre: Pre-topics with no good matches\n",
    "    - unmatched_post: Post-topics with no good matches (potentially new topics)\n",
    "    \"\"\"\n",
    "    \n",
    "    alignments = []\n",
    "    used_post = set()\n",
    "    used_pre = set()\n",
    "    \n",
    "    # Find alignments in order of similarity strength\n",
    "    similarity_coords = []\n",
    "    for i in range(similarity_matrix.shape[0]):\n",
    "        for j in range(similarity_matrix.shape[1]):\n",
    "            if similarity_matrix[i, j] >= threshold:\n",
    "                similarity_coords.append((i, j, similarity_matrix[i, j]))\n",
    "    \n",
    "    # Sort by similarity strength (highest first)\n",
    "    similarity_coords.sort(key=lambda x: x[2], reverse=True)\n",
    "    \n",
    "    # Assign matches, avoiding duplicates\n",
    "    for i, j, sim in similarity_coords:\n",
    "        pre_topic = pre_topics[i]\n",
    "        post_topic = post_topics[j]\n",
    "        \n",
    "        # Check if we've already used these topics\n",
    "        pre_used_count = sum(1 for a in alignments if a[0] == pre_topic)\n",
    "        post_used_count = sum(1 for a in alignments if a[1] == post_topic)\n",
    "        \n",
    "        if pre_used_count < max_matches_per_topic and post_used_count < max_matches_per_topic:\n",
    "            alignments.append((pre_topic, post_topic, sim))\n",
    "            used_pre.add(pre_topic)\n",
    "            used_post.add(post_topic)\n",
    "    \n",
    "    # Find unmatched topics\n",
    "    unmatched_pre = [t for t in pre_topics if t not in used_pre]\n",
    "    unmatched_post = [t for t in post_topics if t not in used_post]\n",
    "    \n",
    "    return alignments, unmatched_pre, unmatched_post\n",
    "\n",
    "def visualize_alignment_matrix(similarity_matrix, pre_topics, post_topics, \n",
    "                              title=\"Topic Alignment Matrix\", figsize=(12, 10)):\n",
    "    \"\"\"\n",
    "    Visualize the topic similarity matrix as a heatmap.\n",
    "    \"\"\"\n",
    "    \n",
    "    plt.figure(figsize=figsize)\n",
    "    \n",
    "    # Create heatmap\n",
    "    sns.heatmap(similarity_matrix, \n",
    "                xticklabels=[f'Post_{t}' for t in post_topics],\n",
    "                yticklabels=[f'Pre_{t}' for t in pre_topics],\n",
    "                annot=False, \n",
    "                cmap='viridis',\n",
    "                cbar_kws={'label': 'Similarity Score'})\n",
    "    \n",
    "    plt.title(title)\n",
    "    plt.xlabel('Post-September Topics')\n",
    "    plt.ylabel('Pre-September Topics')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.yticks(rotation=0)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('topic_alignment_matrix.png', dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "def analyze_alignment_quality(alignments, pre_metrics=None, post_metrics=None):\n",
    "    \"\"\"\n",
    "    Analyze the quality of topic alignments using topic metrics.\n",
    "    \"\"\"\n",
    "    \n",
    "    print(\"=\"*60)\n",
    "    print(\"TOPIC ALIGNMENT ANALYSIS\")\n",
    "    print(\"=\"*60)\n",
    "    \n",
    "    if len(alignments) == 0:\n",
    "        print(\"No alignments found!\")\n",
    "        return\n",
    "    \n",
    "    # Sort alignments by similarity\n",
    "    alignments_sorted = sorted(alignments, key=lambda x: x[2], reverse=True)\n",
    "    \n",
    "    print(f\"\\nFound {len(alignments)} topic alignments:\")\n",
    "    print(f\"{'Pre-Topic':<10} {'Post-Topic':<11} {'Similarity':<10} {'Quality Comparison'}\")\n",
    "    print(\"-\" * 70)\n",
    "    \n",
    "    for pre_topic, post_topic, similarity in alignments_sorted[:30]:  # Show top 30\n",
    "        quality_info = \"\"\n",
    "        \n",
    "        if pre_metrics is not None and post_metrics is not None:\n",
    "            if pre_topic in pre_metrics.index and post_topic in post_metrics.index:\n",
    "                pre_coherence = pre_metrics.loc[pre_topic, 'coherence']\n",
    "                post_coherence = post_metrics.loc[post_topic, 'coherence']\n",
    "                coherence_change = post_coherence - pre_coherence\n",
    "                \n",
    "                if coherence_change > 0:\n",
    "                    quality_info = f\"Improved (+{coherence_change:.1f})\"\n",
    "                else:\n",
    "                    quality_info = f\"Declined ({coherence_change:.1f})\"\n",
    "        \n",
    "        print(f\"{pre_topic:<10} {post_topic:<11} {similarity:<10.3f} {quality_info}\")\n",
    "    \n",
    "    # Similarity distribution\n",
    "    similarities = [sim for _, _, sim in alignments]\n",
    "    \n",
    "    print(f\"\\nAlignment Quality Statistics:\")\n",
    "    print(f\"Mean similarity: {np.mean(similarities):.3f}\")\n",
    "    print(f\"Median similarity: {np.median(similarities):.3f}\")\n",
    "    print(f\"Min similarity: {np.min(similarities):.3f}\")\n",
    "    print(f\"Max similarity: {np.max(similarities):.3f}\")\n",
    "    \n",
    "    # Visualization of alignment quality\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
    "    \n",
    "    # Similarity distribution\n",
    "    ax1.hist(similarities, bins=20, alpha=0.7, edgecolor='black')\n",
    "    ax1.set_title('Distribution of Alignment Similarities')\n",
    "    ax1.set_xlabel('Similarity Score')\n",
    "    ax1.set_ylabel('Number of Alignments')\n",
    "    ax1.axvline(np.mean(similarities), color='red', linestyle='--', \n",
    "                label=f'Mean: {np.mean(similarities):.3f}')\n",
    "    ax1.legend()\n",
    "    \n",
    "    # Top alignments bar chart\n",
    "    top_10 = alignments_sorted[:10]\n",
    "    labels = [f\"{pre}->{post}\" for pre, post, _ in top_10]\n",
    "    sims = [sim for _, _, sim in top_10]\n",
    "    \n",
    "    ax2.barh(labels, sims)\n",
    "    ax2.set_title('Top 10 Topic Alignments')\n",
    "    ax2.set_xlabel('Similarity Score')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('alignment_quality_analysis.png', dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "def run_topic_alignment_analysis(pre_words, post_words, pre_metrics=None, post_metrics=None,\n",
    "                               similarity_method='jaccard', threshold=0.20):\n",
    "    \"\"\"\n",
    "    Run complete topic alignment analysis.\n",
    "    \n",
    "    Parameters:\n",
    "    - pre_words: DataFrame of pre-period topic words\n",
    "    - post_words: DataFrame of post-period topic words\n",
    "    - pre_metrics: DataFrame of pre-period topic metrics (optional)\n",
    "    - post_metrics: DataFrame of post-period topic metrics (optional)\n",
    "    - similarity_method: Method for calculating topic similarity\n",
    "    - threshold: Minimum similarity threshold for alignments\n",
    "    \n",
    "    Returns:\n",
    "    - Dictionary with all results\n",
    "    \"\"\"\n",
    "    \n",
    "    print(\"Starting topic alignment analysis...\")\n",
    "    print(f\"Pre-period topics: {len(pre_words)}\")\n",
    "    print(f\"Post-period topics: {len(post_words)}\")\n",
    "    print(f\"Similarity method: {similarity_method}\")\n",
    "    print(f\"Alignment threshold: {threshold}\")\n",
    "    \n",
    "    # Get topic IDs\n",
    "    pre_topic_ids = list(pre_words.index)\n",
    "    post_topic_ids = list(post_words.index)\n",
    "    \n",
    "    # Calculate similarity matrix\n",
    "    print(\"\\nCalculating topic similarity matrix...\")\n",
    "    similarity_matrix = calculate_topic_similarity_matrix(pre_words, post_words, \n",
    "                                                        method=similarity_method)\n",
    "    \n",
    "    # Visualize similarity matrix\n",
    "    visualize_alignment_matrix(similarity_matrix, pre_topic_ids, post_topic_ids,\n",
    "                             title=f\"Topic Alignment Matrix ({similarity_method})\")\n",
    "    \n",
    "    # Find best alignments\n",
    "    print(\"Finding best topic alignments...\")\n",
    "    alignments, unmatched_pre, unmatched_post = find_best_alignments(\n",
    "        similarity_matrix, pre_topic_ids, post_topic_ids, threshold=threshold\n",
    "    )\n",
    "    \n",
    "    print(f\"\\nAlignment Results:\")\n",
    "    print(f\"Successful alignments: {len(alignments)}\")\n",
    "    print(f\"Unmatched pre-topics: {len(unmatched_pre)}\")\n",
    "    print(f\"Unmatched post-topics: {len(unmatched_post)} (potentially NEW topics)\")\n",
    "    \n",
    "    # Analyze alignment quality\n",
    "    analyze_alignment_quality(alignments, pre_metrics, post_metrics)\n",
    "    \n",
    "    # Print potentially new topics\n",
    "    if unmatched_post:\n",
    "        print(f\"\\nPOTENTIALLY NEW TOPICS (post-September):\")\n",
    "        for topic_id in sorted(unmatched_post):\n",
    "            if post_metrics is not None and topic_id in post_metrics.index:\n",
    "                coherence = post_metrics.loc[topic_id, 'coherence']\n",
    "                tokens = post_metrics.loc[topic_id, 'tokens']\n",
    "                print(f\"Topic {topic_id}: coherence={coherence:.1f}, tokens={tokens:.0f}\")\n",
    "            else:\n",
    "                print(f\"Topic {topic_id}\")\n",
    "    \n",
    "    # Print disappeared topics  \n",
    "    if unmatched_pre:\n",
    "        print(f\"\\nDISAPPEARED/WEAKENED TOPICS (pre-September only):\")\n",
    "        for topic_id in sorted(unmatched_pre):\n",
    "            if pre_metrics is not None and topic_id in pre_metrics.index:\n",
    "                coherence = pre_metrics.loc[topic_id, 'coherence']\n",
    "                tokens = pre_metrics.loc[topic_id, 'tokens']\n",
    "                print(f\"Topic {topic_id}: coherence={coherence:.1f}, tokens={tokens:.0f}\")\n",
    "            else:\n",
    "                print(f\"Topic {topic_id}\")\n",
    "    \n",
    "    return {\n",
    "        'similarity_matrix': similarity_matrix,\n",
    "        'alignments': alignments,\n",
    "        'unmatched_pre': unmatched_pre,\n",
    "        'unmatched_post': unmatched_post,\n",
    "        'pre_topic_ids': pre_topic_ids,\n",
    "        'post_topic_ids': post_topic_ids,\n",
    "        'threshold': threshold,\n",
    "        'method': similarity_method\n",
    "    }\n",
    "\n",
    "# Helper function to examine specific alignments\n",
    "def examine_alignment(pre_words, post_words, pre_topic, post_topic):\n",
    "    \"\"\"\n",
    "    Examine a specific topic alignment in detail.\n",
    "    \"\"\"\n",
    "    \n",
    "    print(f\"ALIGNMENT EXAMINATION: Pre-Topic {pre_topic} <-> Post-Topic {post_topic}\")\n",
    "    print(\"=\"*70)\n",
    "    \n",
    "    if pre_topic in pre_words.index:\n",
    "        pre_words_list = [w for w in pre_words.loc[pre_topic].dropna() if pd.notna(w)]\n",
    "        print(f\"Pre-Topic {pre_topic} words: {', '.join(pre_words_list[:10])}\")\n",
    "    else:\n",
    "        print(f\"Pre-Topic {pre_topic} not found\")\n",
    "        \n",
    "    if post_topic in post_words.index:\n",
    "        post_words_list = [w for w in post_words.loc[post_topic].dropna() if pd.notna(w)]\n",
    "        print(f\"Post-Topic {post_topic} words: {', '.join(post_words_list[:10])}\")\n",
    "    else:\n",
    "        print(f\"Post-Topic {post_topic} not found\")\n",
    "        \n",
    "    if pre_topic in pre_words.index and post_topic in post_words.index:\n",
    "        pre_set = set(pre_words_list)\n",
    "        post_set = set(post_words_list)\n",
    "        common = pre_set & post_set\n",
    "        pre_only = pre_set - post_set\n",
    "        post_only = post_set - pre_set\n",
    "        \n",
    "        print(f\"Common words: {', '.join(list(common)[:10])}\")\n",
    "        print(f\"Pre-only words: {', '.join(list(pre_only)[:10])}\")\n",
    "        print(f\"Post-only words: {', '.join(list(post_only)[:10])}\")\n",
    "\n",
    "print(\"Topic alignment analysis code ready!\")\n",
    "print(\"\\nUsage:\")\n",
    "print(\"1. Load your topic words data:\")\n",
    "print(\"   pre_words = pd.read_csv('pre_topic_words.csv', index_col=0)\")\n",
    "print(\"   post_words = pd.read_csv('post_topic_words.csv', index_col=0)\")\n",
    "print(\"2. Load topic metrics:\")\n",
    "print(\"   pre_metrics = load_topic_metrics('pre_topics.xml')\")\n",
    "print(\"   post_metrics = load_topic_metrics('post_topics.xml')\")\n",
    "print(\"3. Run alignment analysis:\")\n",
    "print(\"   results = run_topic_alignment_analysis(pre_words, post_words, pre_metrics, post_metrics)\")\n",
    "print(\"4. Examine specific alignments:\")\n",
    "print(\"   examine_alignment(pre_words, post_words, 5, 12)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ae1710f4-6c8c-43e6-a321-a9856eeda001",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting topic alignment analysis...\n",
      "Pre-period topics: 74\n",
      "Post-period topics: 74\n",
      "Similarity method: jaccard\n",
      "Alignment threshold: 0.2\n",
      "\n",
      "Calculating topic similarity matrix...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding best topic alignments...\n",
      "\n",
      "Alignment Results:\n",
      "Successful alignments: 121\n",
      "Unmatched pre-topics: 6\n",
      "Unmatched post-topics: 7 (potentially NEW topics)\n",
      "============================================================\n",
      "TOPIC ALIGNMENT ANALYSIS\n",
      "============================================================\n",
      "\n",
      "Found 121 topic alignments:\n",
      "Pre-Topic  Post-Topic  Similarity Quality Comparison\n",
      "----------------------------------------------------------------------\n",
      "65         69          0.905      Improved (+11.5)\n",
      "5          21          0.818      Improved (+6.5)\n",
      "13         38          0.818      Declined (-5.3)\n",
      "26         27          0.818      Improved (+1.5)\n",
      "49         45          0.818      Declined (-29.9)\n",
      "4          16          0.739      Improved (+27.8)\n",
      "55         26          0.739      Declined (-15.6)\n",
      "22         74          0.667      Improved (+37.6)\n",
      "27         22          0.667      Improved (+4.0)\n",
      "59         49          0.667      Improved (+6.7)\n",
      "63         4           0.667      Improved (+0.5)\n",
      "2          53          0.600      Improved (+43.2)\n",
      "12         35          0.600      Declined (-29.3)\n",
      "14         13          0.600      Improved (+36.9)\n",
      "19         23          0.600      Improved (+27.0)\n",
      "29         17          0.600      Improved (+28.5)\n",
      "41         47          0.600      Declined (-10.5)\n",
      "48         28          0.600      Declined (-1.2)\n",
      "54         14          0.600      Improved (+14.9)\n",
      "57         51          0.600      Improved (+86.9)\n",
      "33         56          0.560      Improved (+1.3)\n",
      "23         46          0.538      Declined (-11.5)\n",
      "30         36          0.538      Declined (-19.5)\n",
      "35         67          0.538      Improved (+41.8)\n",
      "42         48          0.538      Improved (+0.3)\n",
      "46         73          0.538      Improved (+2.6)\n",
      "66         59          0.538      Declined (-5.3)\n",
      "3          10          0.481      Improved (+19.3)\n",
      "16         7           0.481      Improved (+13.6)\n",
      "18         19          0.481      Improved (+21.4)\n",
      "\n",
      "Alignment Quality Statistics:\n",
      "Mean similarity: 0.381\n",
      "Median similarity: 0.300\n",
      "Min similarity: 0.212\n",
      "Max similarity: 0.905\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "POTENTIALLY NEW TOPICS (post-September):\n",
      "Topic 11: coherence=-604.5, tokens=196850\n",
      "Topic 15: coherence=-558.6, tokens=205848\n",
      "Topic 20: coherence=-532.2, tokens=276187\n",
      "Topic 32: coherence=-722.3, tokens=166097\n",
      "Topic 37: coherence=-672.4, tokens=239601\n",
      "Topic 62: coherence=-606.9, tokens=227554\n",
      "Topic 66: coherence=-668.1, tokens=168612\n",
      "\n",
      "DISAPPEARED/WEAKENED TOPICS (pre-September only):\n",
      "Topic 6: coherence=-640.5, tokens=256044\n",
      "Topic 36: coherence=-714.8, tokens=212412\n",
      "Topic 38: coherence=-583.9, tokens=504068\n",
      "Topic 43: coherence=-723.3, tokens=280467\n",
      "Topic 61: coherence=-749.6, tokens=271399\n",
      "Topic 68: coherence=-601.2, tokens=398313\n"
     ]
    }
   ],
   "source": [
    "pre_words = pd.read_csv('Data/topic_keys_Before_SquidGame.csv', index_col=0)\n",
    "post_words = pd.read_csv('Data/topic_keys_After_SquidGame.csv', index_col=0)\n",
    "pre_metrics = load_topic_metrics('Data/diagnostics_Before_SquidGame.xml')\n",
    "post_metrics = load_topic_metrics('Data/diagnostics_After_SquidGame.xml')\n",
    "results = run_topic_alignment_analysis(pre_words, post_words, pre_metrics, post_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6109d046-35a8-43e6-89b8-d52416ff9d71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ALIGNMENT EXAMINATION: Pre-Topic 34 <-> Post-Topic 18\n",
      "======================================================================\n",
      "Pre-Topic 34 words: money, people, work, pay, make, job, attention, dont, paid, time\n",
      "Post-Topic 18 words: money, pay, people, make, work, dont, buy, attention, paid, job\n",
      "Common words: paying, people, company, business, making, money, dont, pay, paid, job\n",
      "Pre-only words: give, lot, working, theyre, time, good, youre\n",
      "Post-only words: million, sell, amount, buy, worth, cost, rich\n"
     ]
    }
   ],
   "source": [
    "examine_alignment(pre_words, post_words, 34, 18)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb7fe0ee-46a3-4c81-b64b-b02a1351137a",
   "metadata": {},
   "source": [
    "## Determine Topic Engagement Threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4b100f45-aa64-4f9b-862f-4a49c7caf879",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Threshold Analysis Tools Ready!\n",
      "\n",
      "1. Analyze overall distribution:\n",
      "   threshold_stats = analyze_threshold_selection(pre_topics_filtered)\n",
      "\n",
      "2. Analyze specific topic (e.g., money topic):\n",
      "   threshold_stats = analyze_threshold_selection(pre_topics_filtered, 'topic_34')\n",
      "\n",
      "3. Compare impact of different thresholds:\n",
      "   threshold_comparison = compare_thresholds_impact(\n",
      "       pre_topics_filtered, post_topics_filtered,\n",
      "       thresholds=[0.005, 0.01, 0.02, 0.05]\n",
      "   )\n"
     ]
    }
   ],
   "source": [
    "def analyze_threshold_selection(df, topic_col=None):\n",
    "    \"\"\"\n",
    "    Analyze topic distributions to help choose an appropriate engagement threshold.\n",
    "    \n",
    "    Parameters:\n",
    "    -----------\n",
    "    df : DataFrame\n",
    "        Topic proportions (shows x topics)\n",
    "    topic_col : str, optional\n",
    "        Specific topic to analyze. If None, analyzes all topics.\n",
    "    \"\"\"\n",
    "    \n",
    "    if topic_col:\n",
    "        values = df[topic_col].values  # Convert to numpy array\n",
    "        topic_name = topic_col\n",
    "    else:\n",
    "        values = df.values.flatten()\n",
    "        topic_name = \"All Topics\"\n",
    "    \n",
    "    print(\"=\" * 80)\n",
    "    print(f\"THRESHOLD ANALYSIS: {topic_name}\")\n",
    "    print(\"=\" * 80)\n",
    "    print()\n",
    "    \n",
    "    # Basic statistics - use np functions for numpy arrays\n",
    "    print(\"DISTRIBUTION STATISTICS:\")\n",
    "    print(\"-\" * 80)\n",
    "    print(f\"Mean:              {np.mean(values):.5f}\")\n",
    "    print(f\"Median:            {np.median(values):.5f}\")\n",
    "    print(f\"Std Dev:           {np.std(values):.5f}\")\n",
    "    print(f\"Min:               {np.min(values):.5f}\")\n",
    "    print(f\"Max:               {np.max(values):.5f}\")\n",
    "    print()\n",
    "    \n",
    "    # Percentiles\n",
    "    print(\"PERCENTILES:\")\n",
    "    print(\"-\" * 80)\n",
    "    percentiles = [10, 25, 50, 75, 90, 95, 99]\n",
    "    for p in percentiles:\n",
    "        val = np.percentile(values, p)\n",
    "        print(f\"{p:3d}th percentile:  {val:.5f}\")\n",
    "    print()\n",
    "    \n",
    "    # Test different thresholds\n",
    "    print(\"THRESHOLD IMPACT ANALYSIS:\")\n",
    "    print(\"-\" * 80)\n",
    "    print(f\"{'Threshold':<12} {'% Above':<12} {'Count Above':<15} {'Interpretation'}\")\n",
    "    print(\"-\" * 80)\n",
    "    \n",
    "    thresholds = [0.001, 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0.05, 0.1]\n",
    "    \n",
    "    for thresh in thresholds:\n",
    "        count_above = np.sum(values > thresh)\n",
    "        total = len(values)\n",
    "        pct_above = count_above / total * 100\n",
    "        \n",
    "        # Interpretation\n",
    "        if pct_above > 80:\n",
    "            interp = \"Very inclusive (might include noise)\"\n",
    "        elif pct_above > 50:\n",
    "            interp = \"Moderate (balanced)\"\n",
    "        elif pct_above > 20:\n",
    "            interp = \"Selective (focuses on strong engagement)\"\n",
    "        else:\n",
    "            interp = \"Very selective (only top engagers)\"\n",
    "        \n",
    "        print(f\"{thresh:<12.4f} {pct_above:<11.1f}% {count_above:<15} {interp}\")\n",
    "    \n",
    "    print()\n",
    "    \n",
    "    # Visualize distribution\n",
    "    fig, axes = plt.subplots(2, 2, figsize=(16, 10))\n",
    "    \n",
    "    # 1. Histogram with threshold lines\n",
    "    axes[0,0].hist(values, bins=50, color='steelblue', alpha=0.7, edgecolor='black')\n",
    "    \n",
    "    # Add threshold lines\n",
    "    for thresh, color in [(0.01, 'red'), (0.02, 'orange'), (0.05, 'green')]:\n",
    "        axes[0,0].axvline(thresh, color=color, linestyle='--', linewidth=2, \n",
    "                         label=f'Threshold: {thresh}')\n",
    "    \n",
    "    axes[0,0].set_xlabel('Topic Proportion')\n",
    "    axes[0,0].set_ylabel('Frequency')\n",
    "    axes[0,0].set_title(f'{topic_name}: Distribution of Values')\n",
    "    axes[0,0].legend()\n",
    "    axes[0,0].set_yscale('log')  # Log scale to see tail better\n",
    "    \n",
    "    # 2. Cumulative distribution\n",
    "    sorted_values = np.sort(values)\n",
    "    cumulative = np.arange(1, len(sorted_values) + 1) / len(sorted_values)\n",
    "    \n",
    "    axes[0,1].plot(sorted_values, cumulative, linewidth=2, color='steelblue')\n",
    "    \n",
    "    for thresh, color in [(0.01, 'red'), (0.02, 'orange'), (0.05, 'green')]:\n",
    "        pct_below = np.sum(values <= thresh) / len(values)\n",
    "        axes[0,1].axvline(thresh, color=color, linestyle='--', linewidth=2)\n",
    "        axes[0,1].axhline(pct_below, color=color, linestyle=':', alpha=0.5)\n",
    "        axes[0,1].text(thresh, pct_below + 0.02, f'{pct_below:.1%}', \n",
    "                      fontsize=9, color=color)\n",
    "    \n",
    "    axes[0,1].set_xlabel('Topic Proportion')\n",
    "    axes[0,1].set_ylabel('Cumulative Probability')\n",
    "    axes[0,1].set_title(f'{topic_name}: Cumulative Distribution')\n",
    "    axes[0,1].grid(True, alpha=0.3)\n",
    "    \n",
    "    # 3. Boxplot showing outliers\n",
    "    axes[1,0].boxplot([values], vert=False, widths=0.5)\n",
    "    \n",
    "    for thresh, color in [(0.01, 'red'), (0.02, 'orange'), (0.05, 'green')]:\n",
    "        axes[1,0].axvline(thresh, color=color, linestyle='--', linewidth=2, \n",
    "                         label=f'{thresh}')\n",
    "    \n",
    "    axes[1,0].set_xlabel('Topic Proportion')\n",
    "    axes[1,0].set_title(f'{topic_name}: Boxplot with Threshold Options')\n",
    "    axes[1,0].legend()\n",
    "    axes[1,0].set_yticks([])\n",
    "    \n",
    "    # 4. Threshold sensitivity analysis\n",
    "    if topic_col is None:\n",
    "        # For all topics combined\n",
    "        n_topics = df.shape[1]\n",
    "        n_shows = df.shape[0]\n",
    "        \n",
    "        thresholds_range = np.linspace(0.001, 0.1, 50)\n",
    "        shows_engaged = []\n",
    "        avg_topics_per_show = []\n",
    "        \n",
    "        for t in thresholds_range:\n",
    "            engaged_matrix = df > t\n",
    "            shows_engaged.append(engaged_matrix.any(axis=1).sum())\n",
    "            avg_topics_per_show.append(engaged_matrix.sum(axis=1).mean())\n",
    "        \n",
    "        ax4_1 = axes[1,1]\n",
    "        ax4_2 = ax4_1.twinx()\n",
    "        \n",
    "        line1 = ax4_1.plot(thresholds_range, shows_engaged, 'b-', linewidth=2, \n",
    "                          label='Shows with any topic')\n",
    "        ax4_1.set_xlabel('Threshold')\n",
    "        ax4_1.set_ylabel('# Shows Engaged', color='b')\n",
    "        ax4_1.tick_params(axis='y', labelcolor='b')\n",
    "        \n",
    "        line2 = ax4_2.plot(thresholds_range, avg_topics_per_show, 'r-', linewidth=2,\n",
    "                          label='Avg topics per show')\n",
    "        ax4_2.set_ylabel('Avg Topics per Show', color='r')\n",
    "        ax4_2.tick_params(axis='y', labelcolor='r')\n",
    "        \n",
    "        # Mark common thresholds\n",
    "        for thresh in [0.01, 0.02, 0.05]:\n",
    "            ax4_1.axvline(thresh, color='gray', linestyle='--', alpha=0.5)\n",
    "        \n",
    "        ax4_1.set_title('Threshold Sensitivity Analysis')\n",
    "        ax4_1.grid(True, alpha=0.3)\n",
    "    else:\n",
    "        # For specific topic\n",
    "        thresholds_range = np.linspace(0.001, np.max(values), 50)\n",
    "        n_engaged = [np.sum(values > t) for t in thresholds_range]\n",
    "        \n",
    "        axes[1,1].plot(thresholds_range, n_engaged, linewidth=2, color='steelblue')\n",
    "        axes[1,1].set_xlabel('Threshold')\n",
    "        axes[1,1].set_ylabel('Number of Shows Engaged')\n",
    "        axes[1,1].set_title(f'{topic_name}: Engagement vs Threshold')\n",
    "        axes[1,1].grid(True, alpha=0.3)\n",
    "        \n",
    "        # Mark common thresholds\n",
    "        for thresh, color in [(0.01, 'red'), (0.02, 'orange'), (0.05, 'green')]:\n",
    "            if thresh <= np.max(values):\n",
    "                axes[1,1].axvline(thresh, color=color, linestyle='--', linewidth=2)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'threshold_analysis_{topic_name.replace(\" \", \"_\")}.png', \n",
    "                dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "    \n",
    "    # Recommendations\n",
    "    print(\"=\" * 80)\n",
    "    print(\"RECOMMENDATIONS:\")\n",
    "    print(\"=\" * 80)\n",
    "    \n",
    "    median_val = np.median(values)\n",
    "    percentile_75 = np.percentile(values, 75)\n",
    "    \n",
    "    if median_val < 0.01:\n",
    "        print(f\"✓ 0.01 threshold: REASONABLE\")\n",
    "        print(f\"  Median value is {median_val:.5f}, so 0.01 is above median (selective)\")\n",
    "    else:\n",
    "        print(f\"⚠ 0.01 threshold: TOO LOW\")\n",
    "        print(f\"  Median value is {median_val:.5f}, consider threshold of {median_val:.4f}\")\n",
    "    \n",
    "    print()\n",
    "    print(\"SUGGESTED THRESHOLDS:\")\n",
    "    print(\"-\" * 80)\n",
    "    print(f\"Conservative (inclusive): {np.percentile(values, 50):.4f} (median)\")\n",
    "    print(f\"Moderate (balanced):      {np.percentile(values, 75):.4f} (75th percentile)\")\n",
    "    print(f\"Strict (selective):       {np.percentile(values, 90):.4f} (90th percentile)\")\n",
    "    print()\n",
    "    \n",
    "    return {\n",
    "        'mean': np.mean(values),\n",
    "        'median': np.median(values),\n",
    "        'percentiles': {p: np.percentile(values, p) for p in percentiles},\n",
    "        'recommended_conservative': np.percentile(values, 50),\n",
    "        'recommended_moderate': np.percentile(values, 75),\n",
    "        'recommended_strict': np.percentile(values, 90)\n",
    "    }\n",
    "\n",
    "def compare_thresholds_impact(pre_df, post_df, thresholds=[0.005, 0.01, 0.02, 0.05]):\n",
    "    \"\"\"\n",
    "    Compare how different thresholds affect the engagement rate analysis results.\n",
    "    \"\"\"\n",
    "    \n",
    "    print(\"=\" * 80)\n",
    "    print(\"THRESHOLD COMPARISON: IMPACT ON RESULTS\")\n",
    "    print(\"=\" * 80)\n",
    "    print()\n",
    "    \n",
    "    results = {}\n",
    "    \n",
    "    for thresh in thresholds:\n",
    "        print(f\"\\n{'='*80}\")\n",
    "        print(f\"THRESHOLD: {thresh:.4f}\")\n",
    "        print(f\"{'='*80}\")\n",
    "        \n",
    "        # Count engaged shows\n",
    "        pre_engaged_matrix = pre_df > thresh\n",
    "        post_engaged_matrix = post_df > thresh\n",
    "        \n",
    "        pre_topics_engaged = pre_engaged_matrix.sum(axis=0)\n",
    "        post_topics_engaged = post_engaged_matrix.sum(axis=0)\n",
    "        \n",
    "        pre_shows_engaged = pre_engaged_matrix.sum(axis=1)\n",
    "        post_shows_engaged = post_engaged_matrix.sum(axis=1)\n",
    "        \n",
    "        print(f\"\\nShows per topic (average):\")\n",
    "        print(f\"  Pre-period:  {pre_topics_engaged.mean():.1f} shows\")\n",
    "        print(f\"  Post-period: {post_topics_engaged.mean():.1f} shows\")\n",
    "        \n",
    "        print(f\"\\nTopics per show (average):\")\n",
    "        print(f\"  Pre-period:  {pre_shows_engaged.mean():.1f} topics\")\n",
    "        print(f\"  Post-period: {post_shows_engaged.mean():.1f} topics\")\n",
    "        \n",
    "        # Calculate engagement rates\n",
    "        pre_engagement_rates = pre_topics_engaged / len(pre_df)\n",
    "        post_engagement_rates = post_topics_engaged / len(post_df)\n",
    "        engagement_changes = post_engagement_rates - pre_engagement_rates\n",
    "        \n",
    "        # Count status categories\n",
    "        strengthened = (engagement_changes > 0.10).sum()\n",
    "        weakened = (engagement_changes < -0.10).sum()\n",
    "        stable = len(engagement_changes) - strengthened - weakened\n",
    "        \n",
    "        print(f\"\\nTopic status at this threshold:\")\n",
    "        print(f\"  Strengthened: {strengthened} topics\")\n",
    "        print(f\"  Weakened:     {weakened} topics\")\n",
    "        print(f\"  Stable:       {stable} topics\")\n",
    "        \n",
    "        results[thresh] = {\n",
    "            'avg_shows_per_topic_pre': pre_topics_engaged.mean(),\n",
    "            'avg_shows_per_topic_post': post_topics_engaged.mean(),\n",
    "            'avg_topics_per_show_pre': pre_shows_engaged.mean(),\n",
    "            'avg_topics_per_show_post': post_shows_engaged.mean(),\n",
    "            'strengthened': strengthened,\n",
    "            'weakened': weakened,\n",
    "            'stable': stable\n",
    "        }\n",
    "    \n",
    "    # Visualization\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "    \n",
    "    # Plot 1: Average engagement metrics\n",
    "    thresh_labels = [f'{t:.3f}' for t in thresholds]\n",
    "    \n",
    "    axes[0].plot(thresh_labels, [results[t]['avg_topics_per_show_pre'] for t in thresholds],\n",
    "                marker='o', label='Pre: Topics/Show', linewidth=2)\n",
    "    axes[0].plot(thresh_labels, [results[t]['avg_topics_per_show_post'] for t in thresholds],\n",
    "                marker='o', label='Post: Topics/Show', linewidth=2)\n",
    "    axes[0].set_xlabel('Threshold')\n",
    "    axes[0].set_ylabel('Average Topics per Show')\n",
    "    axes[0].set_title('Impact of Threshold on Topic Breadth')\n",
    "    axes[0].legend()\n",
    "    axes[0].grid(True, alpha=0.3)\n",
    "    \n",
    "    # Plot 2: Status categories\n",
    "    strengthened_counts = [results[t]['strengthened'] for t in thresholds]\n",
    "    weakened_counts = [results[t]['weakened'] for t in thresholds]\n",
    "    stable_counts = [results[t]['stable'] for t in thresholds]\n",
    "    \n",
    "    x = np.arange(len(thresholds))\n",
    "    width = 0.25\n",
    "    \n",
    "    axes[1].bar(x - width, strengthened_counts, width, label='Strengthened', color='green', alpha=0.7)\n",
    "    axes[1].bar(x, stable_counts, width, label='Stable', color='gray', alpha=0.7)\n",
    "    axes[1].bar(x + width, weakened_counts, width, label='Weakened', color='red', alpha=0.7)\n",
    "    \n",
    "    axes[1].set_xlabel('Threshold')\n",
    "    axes[1].set_ylabel('Number of Topics')\n",
    "    axes[1].set_title('Impact of Threshold on Topic Classification')\n",
    "    axes[1].set_xticks(x)\n",
    "    axes[1].set_xticklabels(thresh_labels)\n",
    "    axes[1].legend()\n",
    "    axes[1].grid(True, alpha=0.3, axis='y')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('threshold_comparison_impact.png', dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "    \n",
    "    return results\n",
    "\n",
    "\n",
    "# Usage instructions\n",
    "print(\"\\nThreshold Analysis Tools Ready!\")\n",
    "print(\"\\n1. Analyze overall distribution:\")\n",
    "print(\"   threshold_stats = analyze_threshold_selection(pre_topics_filtered)\")\n",
    "print(\"\\n2. Analyze specific topic (e.g., money topic):\")\n",
    "print(\"   threshold_stats = analyze_threshold_selection(pre_topics_filtered, 'topic_34')\")\n",
    "print(\"\\n3. Compare impact of different thresholds:\")\n",
    "print(\"   threshold_comparison = compare_thresholds_impact(\")\n",
    "print(\"       pre_topics_filtered, post_topics_filtered,\")\n",
    "print(\"       thresholds=[0.005, 0.01, 0.02, 0.05]\")\n",
    "print(\"   )\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0c5dd2cd-8a31-411b-aee5-a899e9836d69",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "THRESHOLD ANALYSIS: All Topics\n",
      "================================================================================\n",
      "\n",
      "DISTRIBUTION STATISTICS:\n",
      "--------------------------------------------------------------------------------\n",
      "Mean:              0.01333\n",
      "Median:            0.01285\n",
      "Std Dev:           0.00646\n",
      "Min:               0.00245\n",
      "Max:               0.08698\n",
      "\n",
      "PERCENTILES:\n",
      "--------------------------------------------------------------------------------\n",
      " 10th percentile:  0.00637\n",
      " 25th percentile:  0.00877\n",
      " 50th percentile:  0.01285\n",
      " 75th percentile:  0.01663\n",
      " 90th percentile:  0.02029\n",
      " 95th percentile:  0.02308\n",
      " 99th percentile:  0.03383\n",
      "\n",
      "THRESHOLD IMPACT ANALYSIS:\n",
      "--------------------------------------------------------------------------------\n",
      "Threshold    % Above      Count Above     Interpretation\n",
      "--------------------------------------------------------------------------------\n",
      "0.0010       100.0      % 3000            Very inclusive (might include noise)\n",
      "0.0050       95.8       % 2875            Very inclusive (might include noise)\n",
      "0.0100       65.9       % 1976            Moderate (balanced)\n",
      "0.0150       34.8       % 1044            Selective (focuses on strong engagement)\n",
      "0.0200       10.7       % 322             Very selective (only top engagers)\n",
      "0.0250       3.3        % 99              Very selective (only top engagers)\n",
      "0.0300       1.7        % 51              Very selective (only top engagers)\n",
      "0.0500       0.3        % 9               Very selective (only top engagers)\n",
      "0.1000       0.0        % 0               Very selective (only top engagers)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "RECOMMENDATIONS:\n",
      "================================================================================\n",
      "⚠ 0.01 threshold: TOO LOW\n",
      "  Median value is 0.01285, consider threshold of 0.0128\n",
      "\n",
      "SUGGESTED THRESHOLDS:\n",
      "--------------------------------------------------------------------------------\n",
      "Conservative (inclusive): 0.0128 (median)\n",
      "Moderate (balanced):      0.0166 (75th percentile)\n",
      "Strict (selective):       0.0203 (90th percentile)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#analyze topic engagment threshold for preSG data\n",
    "threshold_stats = analyze_threshold_selection(pre_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "310f8bac-79a1-40a8-a3e5-77ace70a8faf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "THRESHOLD ANALYSIS: All Topics\n",
      "================================================================================\n",
      "\n",
      "DISTRIBUTION STATISTICS:\n",
      "--------------------------------------------------------------------------------\n",
      "Mean:              0.01333\n",
      "Median:            0.01272\n",
      "Std Dev:           0.00668\n",
      "Min:               0.00336\n",
      "Max:               0.08522\n",
      "\n",
      "PERCENTILES:\n",
      "--------------------------------------------------------------------------------\n",
      " 10th percentile:  0.00649\n",
      " 25th percentile:  0.00912\n",
      " 50th percentile:  0.01272\n",
      " 75th percentile:  0.01606\n",
      " 90th percentile:  0.01931\n",
      " 95th percentile:  0.02278\n",
      " 99th percentile:  0.04128\n",
      "\n",
      "THRESHOLD IMPACT ANALYSIS:\n",
      "--------------------------------------------------------------------------------\n",
      "Threshold    % Above      Count Above     Interpretation\n",
      "--------------------------------------------------------------------------------\n",
      "0.0010       100.0      % 1575            Very inclusive (might include noise)\n",
      "0.0050       96.4       % 1519            Very inclusive (might include noise)\n",
      "0.0100       69.3       % 1091            Moderate (balanced)\n",
      "0.0150       34.2       % 539             Selective (focuses on strong engagement)\n",
      "0.0200       8.5        % 134             Very selective (only top engagers)\n",
      "0.0250       3.2        % 51              Very selective (only top engagers)\n",
      "0.0300       1.7        % 27              Very selective (only top engagers)\n",
      "0.0500       0.5        % 8               Very selective (only top engagers)\n",
      "0.1000       0.0        % 0               Very selective (only top engagers)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "RECOMMENDATIONS:\n",
      "================================================================================\n",
      "⚠ 0.01 threshold: TOO LOW\n",
      "  Median value is 0.01272, consider threshold of 0.0127\n",
      "\n",
      "SUGGESTED THRESHOLDS:\n",
      "--------------------------------------------------------------------------------\n",
      "Conservative (inclusive): 0.0127 (median)\n",
      "Moderate (balanced):      0.0161 (75th percentile)\n",
      "Strict (selective):       0.0193 (90th percentile)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#analyze topic engagment threshold for preSG data\n",
    "threshold_stats = analyze_threshold_selection(post_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83c12e75-2c76-4f9c-a7c8-2acd69e39163",
   "metadata": {},
   "source": [
    "## Analyze Topic Strength Pre/Post Squid Game with Aligned Topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b7292774-7b60-4c67-aeb4-09ee125b4ae4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Aligned Engagement Rate Analysis Ready!\n",
      "\n",
      "All functions updated to handle string column names correctly.\n"
     ]
    }
   ],
   "source": [
    "def analyze_topic_engagement_rates_with_alignment(pre_df, post_df, alignment_results, \n",
    "                                                  threshold=0.01, \n",
    "                                                  engagement_change_threshold=0.10):\n",
    "    \"\"\"\n",
    "    Compare topics based on engagement rates, using alignment results to match topics.\n",
    "    Handles string column names (e.g., '0', '1', '34', '18').\n",
    "    \"\"\"\n",
    "    \n",
    "    alignments = alignment_results['alignments']\n",
    "    unmatched_pre = alignment_results['unmatched_pre']\n",
    "    unmatched_post = alignment_results['unmatched_post']\n",
    "    \n",
    "    n_pre_shows = len(pre_df)\n",
    "    n_post_shows = len(post_df)\n",
    "    \n",
    "    print(\"=\" * 80)\n",
    "    print(\"TOPIC ENGAGEMENT RATE ANALYSIS (WITH ALIGNMENT)\")\n",
    "    print(\"=\" * 80)\n",
    "    print(f\"Pre-period shows:  {n_pre_shows}\")\n",
    "    print(f\"Post-period shows: {n_post_shows}\")\n",
    "    print(f\"Aligned topic pairs: {len(alignments)}\")\n",
    "    print(f\"Unmatched pre-topics (disappeared): {len(unmatched_pre)}\")\n",
    "    print(f\"Unmatched post-topics (emerged): {len(unmatched_post)}\")\n",
    "    print(f\"Engagement threshold: {threshold:.3f}\")\n",
    "    print(f\"Change threshold for classification: {engagement_change_threshold:.2%}\")\n",
    "    print()\n",
    "    \n",
    "    # Process aligned topics\n",
    "    aligned_results = []\n",
    "    skipped_count = 0\n",
    "    \n",
    "    for pre_topic, post_topic, similarity in alignments:\n",
    "        # Convert to string (columns are strings)\n",
    "        pre_col = str(pre_topic)\n",
    "        post_col = str(post_topic)\n",
    "        \n",
    "        # Check if columns exist\n",
    "        if pre_col not in pre_df.columns:\n",
    "            print(f\"Warning: Pre-topic '{pre_col}' not found in pre_df columns\")\n",
    "            skipped_count += 1\n",
    "            continue\n",
    "        \n",
    "        if post_col not in post_df.columns:\n",
    "            print(f\"Warning: Post-topic '{post_col}' not found in post_df columns\")\n",
    "            skipped_count += 1\n",
    "            continue\n",
    "        \n",
    "        # Get topic data\n",
    "        pre_values = pre_df[pre_col]\n",
    "        post_values = post_df[post_col]\n",
    "        \n",
    "        # Calculate engagement metrics\n",
    "        pre_count = (pre_values > threshold).sum()\n",
    "        post_count = (post_values > threshold).sum()\n",
    "        \n",
    "        pre_rate = pre_count / n_pre_shows\n",
    "        post_rate = post_count / n_post_shows\n",
    "        \n",
    "        # Mean strength\n",
    "        pre_mean_all = pre_values.mean()\n",
    "        post_mean_all = post_values.mean()\n",
    "        \n",
    "        # Mean strength among engaged shows\n",
    "        pre_strength_engaged = pre_values[pre_values > threshold].mean() if (pre_values > threshold).any() else 0\n",
    "        post_strength_engaged = post_values[post_values > threshold].mean() if (post_values > threshold).any() else 0\n",
    "        \n",
    "        aligned_results.append({\n",
    "            'pre_topic': str(pre_topic),  # Store as string for consistency\n",
    "            'post_topic': str(post_topic),  # Store as string for consistency\n",
    "            'alignment_similarity': similarity,\n",
    "            'pre_engagement_rate': pre_rate,\n",
    "            'post_engagement_rate': post_rate,\n",
    "            'pre_shows_engaged': pre_count,\n",
    "            'post_shows_engaged': post_count,\n",
    "            'pre_mean_all_shows': pre_mean_all,\n",
    "            'post_mean_all_shows': post_mean_all,\n",
    "            'pre_strength_if_engaged': pre_strength_engaged,\n",
    "            'post_strength_if_engaged': post_strength_engaged,\n",
    "            'engagement_rate_change': post_rate - pre_rate,\n",
    "            'engagement_rate_pct_change': ((post_rate - pre_rate) / pre_rate * 100) if pre_rate > 0 else np.inf\n",
    "        })\n",
    "    \n",
    "    if skipped_count > 0:\n",
    "        print(f\"⚠️  WARNING: Skipped {skipped_count} alignments due to missing columns\")\n",
    "    \n",
    "    if not aligned_results:\n",
    "        print(\"\\n❌ ERROR: No alignments could be processed!\")\n",
    "        return None\n",
    "    \n",
    "    print(f\"✓ Successfully processed {len(aligned_results)} aligned topic pairs\")\n",
    "    \n",
    "    comparison = pd.DataFrame(aligned_results)\n",
    "    comparison['topic_pair'] = comparison.apply(lambda x: f\"{x['pre_topic']}->{x['post_topic']}\", axis=1)\n",
    "    comparison = comparison.set_index('topic_pair')\n",
    "    \n",
    "    # Classify aligned topics\n",
    "    comparison['status'] = 'stable'\n",
    "    comparison.loc[comparison['engagement_rate_change'] > engagement_change_threshold, 'status'] = 'strengthened'\n",
    "    comparison.loc[comparison['engagement_rate_change'] < -engagement_change_threshold, 'status'] = 'weakened'\n",
    "    \n",
    "    # Add emerged topics (unmatched post topics)\n",
    "    emerged_results = []\n",
    "    for post_topic in unmatched_post:\n",
    "        post_col = str(post_topic)\n",
    "        \n",
    "        if post_col not in post_df.columns:\n",
    "            continue\n",
    "        \n",
    "        post_values = post_df[post_col]\n",
    "        post_count = (post_values > threshold).sum()\n",
    "        post_rate = post_count / n_post_shows\n",
    "        post_mean_all = post_values.mean()\n",
    "        post_strength_engaged = post_values[post_values > threshold].mean() if (post_values > threshold).any() else 0\n",
    "        \n",
    "        emerged_results.append({\n",
    "            'pre_topic': None,\n",
    "            'post_topic': str(post_topic),  # Store as string\n",
    "            'alignment_similarity': 0,\n",
    "            'pre_engagement_rate': 0,\n",
    "            'post_engagement_rate': post_rate,\n",
    "            'pre_shows_engaged': 0,\n",
    "            'post_shows_engaged': post_count,\n",
    "            'pre_mean_all_shows': 0,\n",
    "            'post_mean_all_shows': post_mean_all,\n",
    "            'pre_strength_if_engaged': 0,\n",
    "            'post_strength_if_engaged': post_strength_engaged,\n",
    "            'engagement_rate_change': post_rate,\n",
    "            'engagement_rate_pct_change': np.inf,\n",
    "            'status': 'emerged',\n",
    "            'topic_pair': f\"NEW->{post_topic}\"\n",
    "        })\n",
    "    \n",
    "    if emerged_results:\n",
    "        emerged_df = pd.DataFrame(emerged_results).set_index('topic_pair')\n",
    "        comparison = pd.concat([comparison, emerged_df])\n",
    "        print(f\"✓ Added {len(emerged_results)} emerged topics\")\n",
    "    \n",
    "    # Add disappeared topics (unmatched pre topics)\n",
    "    disappeared_results = []\n",
    "    for pre_topic in unmatched_pre:\n",
    "        pre_col = str(pre_topic)\n",
    "        \n",
    "        if pre_col not in pre_df.columns:\n",
    "            continue\n",
    "        \n",
    "        pre_values = pre_df[pre_col]\n",
    "        pre_count = (pre_values > threshold).sum()\n",
    "        pre_rate = pre_count / n_pre_shows\n",
    "        pre_mean_all = pre_values.mean()\n",
    "        pre_strength_engaged = pre_values[pre_values > threshold].mean() if (pre_values > threshold).any() else 0\n",
    "        \n",
    "        disappeared_results.append({\n",
    "            'pre_topic': str(pre_topic),  # Store as string\n",
    "            'post_topic': None,\n",
    "            'alignment_similarity': 0,\n",
    "            'pre_engagement_rate': pre_rate,\n",
    "            'post_engagement_rate': 0,\n",
    "            'pre_shows_engaged': pre_count,\n",
    "            'post_shows_engaged': 0,\n",
    "            'pre_mean_all_shows': pre_mean_all,\n",
    "            'post_mean_all_shows': 0,\n",
    "            'pre_strength_if_engaged': pre_strength_engaged,\n",
    "            'post_strength_if_engaged': 0,\n",
    "            'engagement_rate_change': -pre_rate,\n",
    "            'engagement_rate_pct_change': -100,\n",
    "            'status': 'disappeared',\n",
    "            'topic_pair': f\"{pre_topic}->GONE\"\n",
    "        })\n",
    "    \n",
    "    if disappeared_results:\n",
    "        disappeared_df = pd.DataFrame(disappeared_results).set_index('topic_pair')\n",
    "        comparison = pd.concat([comparison, disappeared_df])\n",
    "        print(f\"✓ Added {len(disappeared_results)} disappeared topics\")\n",
    "    \n",
    "    # Summary statistics\n",
    "    print()\n",
    "    print(\"=\" * 80)\n",
    "    print(\"TOPIC STATUS SUMMARY\")\n",
    "    print(\"=\" * 80)\n",
    "    status_counts = comparison['status'].value_counts()\n",
    "    for status in ['emerged', 'strengthened', 'stable', 'weakened', 'disappeared']:\n",
    "        if status in status_counts.index:\n",
    "            count = status_counts[status]\n",
    "            print(f\"{status.capitalize():15s}: {count:2d} topics ({count/len(comparison)*100:5.1f}%)\")\n",
    "    print()\n",
    "    \n",
    "    return comparison.sort_values('engagement_rate_change', ascending=False)\n",
    "\n",
    "\n",
    "def analyze_show_engagement_patterns_aligned(comparison_df, pre_df, post_df, \n",
    "                                             focus_show='Squid Game', threshold=0.01):\n",
    "    \"\"\"\n",
    "    Analyze show engagement patterns using aligned topics.\n",
    "    FIXED: Properly handles string column names.\n",
    "    \"\"\"\n",
    "    \n",
    "    print(\"\\n\" + \"=\" * 80)\n",
    "    print(f\"SHOW-LEVEL ENGAGEMENT ANALYSIS: {focus_show}\")\n",
    "    print(\"=\" * 80)\n",
    "    \n",
    "    # Check if focus show exists in post-period\n",
    "    if focus_show not in post_df.index:\n",
    "        print(f\"\\nWARNING: '{focus_show}' not found in post-period data.\")\n",
    "        print(\"Available shows in post-period:\")\n",
    "        print(post_df.index.tolist())\n",
    "        return None\n",
    "    \n",
    "    # Get all post topics from comparison (handling both aligned and emerged)\n",
    "    # Map topic_pair index to post_topic column name\n",
    "    post_topics_map = {}  # Maps topic_pair -> post_column_name\n",
    "    \n",
    "    for idx, row in comparison_df.iterrows():\n",
    "        if pd.notna(row['post_topic']):\n",
    "            post_col = str(row['post_topic'])  # Column name is string\n",
    "            if post_col in post_df.columns:\n",
    "                post_topics_map[idx] = post_col\n",
    "    \n",
    "    print(f\"Found {len(post_topics_map)} post-period topics to analyze\")\n",
    "    \n",
    "    # Get focus show's engagement for all post topics\n",
    "    focus_post_engagement = {}\n",
    "    for pair_idx, post_col in post_topics_map.items():\n",
    "        focus_post_engagement[pair_idx] = post_df.loc[focus_show, post_col]\n",
    "    \n",
    "    focus_engaged_count = sum(1 for v in focus_post_engagement.values() if v > threshold)\n",
    "    \n",
    "    print(f\"\\n{focus_show} engages with {focus_engaged_count} topics (>{threshold:.3f} threshold)\")\n",
    "    print()\n",
    "    \n",
    "    # Analyze focus show's engagement by topic status\n",
    "    topic_status_engagement = {}\n",
    "    \n",
    "    for status in comparison_df['status'].unique():\n",
    "        status_rows = comparison_df[comparison_df['status'] == status]\n",
    "        \n",
    "        engaged_count = 0\n",
    "        total_strength = 0\n",
    "        engaged_strengths = []\n",
    "        \n",
    "        for idx, row in status_rows.iterrows():\n",
    "            if idx in focus_post_engagement:\n",
    "                strength = focus_post_engagement[idx]\n",
    "                total_strength += strength\n",
    "                if strength > threshold:\n",
    "                    engaged_count += 1\n",
    "                    engaged_strengths.append(strength)\n",
    "        \n",
    "        topic_status_engagement[status] = {\n",
    "            'n_topics': len(status_rows),\n",
    "            'n_engaged': engaged_count,\n",
    "            'engagement_rate': engaged_count / len(status_rows) if len(status_rows) > 0 else 0,\n",
    "            'mean_strength': total_strength / len(status_rows) if len(status_rows) > 0 else 0,\n",
    "            'mean_strength_if_engaged': np.mean(engaged_strengths) if engaged_strengths else 0\n",
    "        }\n",
    "    \n",
    "    # Print summary table\n",
    "    print(f\"\\n{focus_show}'s Engagement by Topic Status:\")\n",
    "    print(\"-\" * 80)\n",
    "    print(f\"{'Status':<20} {'Topics':<10} {'Engaged':<10} {'Rate':<12} {'Mean Strength'}\")\n",
    "    print(\"-\" * 80)\n",
    "    \n",
    "    for status in ['emerged', 'strengthened', 'stable', 'weakened', 'disappeared']:\n",
    "        if status in topic_status_engagement:\n",
    "            data = topic_status_engagement[status]\n",
    "            print(f\"{status.capitalize():<20} {data['n_topics']:<10} {data['n_engaged']:<10} \"\n",
    "                  f\"{data['engagement_rate']:<11.1%} {data['mean_strength']:.4f}\")\n",
    "    \n",
    "    # Top emerging/strengthened topics for focus show\n",
    "    print(f\"\\n\\n{'=' * 80}\")\n",
    "    print(f\"TOP EMERGING/STRENGTHENED TOPICS FOR {focus_show}\")\n",
    "    print(\"=\" * 80)\n",
    "    \n",
    "    emerging_strengthened = comparison_df[\n",
    "        comparison_df['status'].isin(['emerged', 'strengthened'])\n",
    "    ]\n",
    "    \n",
    "    focus_emerging_data = []\n",
    "    for idx, row in emerging_strengthened.iterrows():\n",
    "        if idx in focus_post_engagement:\n",
    "            strength = focus_post_engagement[idx]\n",
    "            if strength > threshold:\n",
    "                focus_emerging_data.append({\n",
    "                    'topic_pair': idx,\n",
    "                    'strength': strength,\n",
    "                    'status': row['status'],\n",
    "                    'change': row['engagement_rate_change'],\n",
    "                    'post_rate': row['post_engagement_rate'],\n",
    "                    'post_topic': row['post_topic']\n",
    "                })\n",
    "    \n",
    "    focus_emerging_df = pd.DataFrame(focus_emerging_data).sort_values('strength', ascending=False)\n",
    "    \n",
    "    print(f\"\\n{focus_show} engages with {len(focus_emerging_df)} out of \"\n",
    "          f\"{len(emerging_strengthened)} emerging/strengthened topics:\\n\")\n",
    "    \n",
    "    for i, (_, row) in enumerate(focus_emerging_df.head(15).iterrows(), 1):\n",
    "        print(f\"{i:2d}. {row['topic_pair']}\")\n",
    "        print(f\"    Status: {row['status'].capitalize()} (+{row['change']:.1%} engagement rate change)\")\n",
    "        print(f\"    {focus_show} strength: {row['strength']:.4f}\")\n",
    "        print(f\"    Overall post-period engagement: {row['post_rate']:.1%} of shows\")\n",
    "        print()\n",
    "    \n",
    "    # Rankings for emerging topics\n",
    "    print(f\"\\n{'=' * 80}\")\n",
    "    print(f\"{focus_show}'S RANK IN EMERGING/STRENGTHENED TOPICS\")\n",
    "    print(\"=\" * 80)\n",
    "    \n",
    "    rankings = []\n",
    "    for idx, row in emerging_strengthened.iterrows():\n",
    "        if pd.notna(row['post_topic']):\n",
    "            post_col = str(row['post_topic'])  # Convert to string\n",
    "            \n",
    "            if post_col in post_df.columns:\n",
    "                topic_strengths = post_df[post_col].sort_values(ascending=False)\n",
    "                if focus_show in topic_strengths.index:\n",
    "                    rank = list(topic_strengths.index).index(focus_show) + 1\n",
    "                    rankings.append({\n",
    "                        'topic_pair': idx,\n",
    "                        'post_topic': row['post_topic'],\n",
    "                        'rank': rank,\n",
    "                        'total_shows': len(topic_strengths),\n",
    "                        'strength': topic_strengths[focus_show],\n",
    "                        'top_show': topic_strengths.index[0],\n",
    "                        'top_strength': topic_strengths.iloc[0]\n",
    "                    })\n",
    "    \n",
    "    rankings_df = pd.DataFrame(rankings).sort_values('rank')\n",
    "    \n",
    "    print(f\"\\nTopics where {focus_show} ranks highest:\\n\")\n",
    "    for i, (_, row) in enumerate(rankings_df.head(10).iterrows(), 1):\n",
    "        print(f\"{row['topic_pair']} (Post-Topic {row['post_topic']})\")\n",
    "        print(f\"  Rank: #{row['rank']:.0f} out of {row['total_shows']:.0f} shows \"\n",
    "              f\"(strength: {row['strength']:.4f})\")\n",
    "        if row['rank'] > 1:\n",
    "            print(f\"  Top show: {row['top_show']} (strength: {row['top_strength']:.4f})\")\n",
    "        else:\n",
    "            print(f\"  >>> {focus_show} is #1 for this topic! <<<\")\n",
    "        print()\n",
    "    \n",
    "    # Overall leadership score\n",
    "    top_5_topics = sum(1 for _, r in rankings_df.iterrows() if r['rank'] <= 5)\n",
    "    top_10_topics = sum(1 for _, r in rankings_df.iterrows() if r['rank'] <= 10)\n",
    "    median_rank = rankings_df['rank'].median() if len(rankings_df) > 0 else None\n",
    "    \n",
    "    print(f\"\\n{'=' * 80}\")\n",
    "    print(f\"{focus_show} LEADERSHIP SUMMARY\")\n",
    "    print(\"=\" * 80)\n",
    "    print(f\"Topics ranked in top 5:  {top_5_topics} out of {len(rankings_df)}\")\n",
    "    print(f\"Topics ranked in top 10: {top_10_topics} out of {len(rankings_df)}\")\n",
    "    if median_rank:\n",
    "        print(f\"Median rank across emerging/strengthened topics: {median_rank:.1f}\")\n",
    "    \n",
    "    return {\n",
    "        'status_engagement': topic_status_engagement,\n",
    "        'rankings': rankings_df,\n",
    "        'top_5_count': top_5_topics,\n",
    "        'top_10_count': top_10_topics,\n",
    "        'median_rank': median_rank\n",
    "    }\n",
    "\n",
    "\n",
    "def visualize_show_emergence_role(comparison_df, post_df, focus_show='Squid Game', \n",
    "                                  threshold=0.01, top_n_shows=15):\n",
    "    \"\"\"\n",
    "    Create visualizations showing which shows drive emerging/strengthened topics.\n",
    "    FIXED: Properly handles string column names and aligned topics.\n",
    "    \"\"\"\n",
    "    \n",
    "    fig, axes = plt.subplots(2, 2, figsize=(18, 12))\n",
    "    \n",
    "    # Get emerging/strengthened topics\n",
    "    emerging_topics = comparison_df[\n",
    "        comparison_df['status'].isin(['emerged', 'strengthened'])\n",
    "    ]\n",
    "    \n",
    "    if len(emerging_topics) == 0:\n",
    "        print(\"No emerging or strengthened topics found!\")\n",
    "        return\n",
    "    \n",
    "    # Get post topic columns (as strings)\n",
    "    emerging_post_cols = []\n",
    "    for idx, row in emerging_topics.iterrows():\n",
    "        if pd.notna(row['post_topic']):\n",
    "            post_col = str(row['post_topic'])\n",
    "            if post_col in post_df.columns:\n",
    "                emerging_post_cols.append(post_col)\n",
    "    \n",
    "    if not emerging_post_cols:\n",
    "        print(\"No valid post-period columns found for emerging topics!\")\n",
    "        return\n",
    "    \n",
    "    # 1. Show engagement with emerging topics\n",
    "    show_emerging_engagement = post_df[emerging_post_cols].sum(axis=1).sort_values(ascending=False)\n",
    "    top_shows = show_emerging_engagement.head(top_n_shows)\n",
    "    \n",
    "    colors = ['red' if show == focus_show else 'steelblue' for show in top_shows.index]\n",
    "    axes[0,0].barh(range(len(top_shows)), top_shows.values, color=colors, alpha=0.7)\n",
    "    axes[0,0].set_yticks(range(len(top_shows)))\n",
    "    axes[0,0].set_yticklabels(top_shows.index, fontsize=9)\n",
    "    axes[0,0].set_xlabel('Total Engagement Strength')\n",
    "    axes[0,0].set_title(f'Top {top_n_shows} Shows: Engagement with Emerging/Strengthened Topics')\n",
    "    axes[0,0].invert_yaxis()\n",
    "    \n",
    "    # Add ranking annotation for focus show\n",
    "    if focus_show in show_emerging_engagement.index:\n",
    "        focus_rank = list(show_emerging_engagement.index).index(focus_show) + 1\n",
    "        axes[0,0].text(0.98, 0.02, f'{focus_show} rank: #{focus_rank}', \n",
    "                      transform=axes[0,0].transAxes, ha='right', va='bottom',\n",
    "                      bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8),\n",
    "                      fontsize=10, fontweight='bold')\n",
    "    \n",
    "    # 2. Number of emerging topics each show engages with\n",
    "    show_emerging_count = (post_df[emerging_post_cols] > threshold).sum(axis=1).sort_values(ascending=False)\n",
    "    top_shows_count = show_emerging_count.head(top_n_shows)\n",
    "    \n",
    "    colors = ['red' if show == focus_show else 'steelblue' for show in top_shows_count.index]\n",
    "    axes[0,1].barh(range(len(top_shows_count)), top_shows_count.values, color=colors, alpha=0.7)\n",
    "    axes[0,1].set_yticks(range(len(top_shows_count)))\n",
    "    axes[0,1].set_yticklabels(top_shows_count.index, fontsize=9)\n",
    "    axes[0,1].set_xlabel('Number of Topics Engaged')\n",
    "    axes[0,1].set_title(f'Top {top_n_shows} Shows: Breadth of Engagement\\n(# of emerging/strengthened topics)')\n",
    "    axes[0,1].invert_yaxis()\n",
    "    \n",
    "    if focus_show in show_emerging_count.index:\n",
    "        focus_rank = list(show_emerging_count.index).index(focus_show) + 1\n",
    "        axes[0,1].text(0.98, 0.02, f'{focus_show} rank: #{focus_rank}', \n",
    "                      transform=axes[0,1].transAxes, ha='right', va='bottom',\n",
    "                      bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8),\n",
    "                      fontsize=10, fontweight='bold')\n",
    "    \n",
    "    # 3. Focus show's engagement across topic statuses\n",
    "    if focus_show in post_df.index:\n",
    "        focus_data = []\n",
    "        for status in ['emerged', 'strengthened', 'stable', 'weakened']:\n",
    "            status_topics = comparison_df[comparison_df['status'] == status]\n",
    "            \n",
    "            # Get post columns for this status\n",
    "            status_post_cols = []\n",
    "            for idx, row in status_topics.iterrows():\n",
    "                if pd.notna(row['post_topic']):\n",
    "                    post_col = str(row['post_topic'])\n",
    "                    if post_col in post_df.columns:\n",
    "                        status_post_cols.append(post_col)\n",
    "            \n",
    "            if status_post_cols:\n",
    "                n_engaged = (post_df.loc[focus_show, status_post_cols] > threshold).sum()\n",
    "                n_total = len(status_post_cols)\n",
    "                focus_data.append({\n",
    "                    'status': status,\n",
    "                    'n_engaged': n_engaged,\n",
    "                    'n_total': n_total,\n",
    "                    'rate': n_engaged / n_total if n_total > 0 else 0\n",
    "                })\n",
    "        \n",
    "        if focus_data:\n",
    "            focus_df = pd.DataFrame(focus_data)\n",
    "            colors_status = {'emerged': 'darkgreen', 'strengthened': 'green', \n",
    "                            'stable': 'gray', 'weakened': 'red'}\n",
    "            bar_colors = [colors_status.get(s, 'blue') for s in focus_df['status']]\n",
    "            \n",
    "            axes[1,0].bar(range(len(focus_df)), focus_df['rate'], color=bar_colors, alpha=0.7)\n",
    "            axes[1,0].set_xticks(range(len(focus_df)))\n",
    "            axes[1,0].set_xticklabels(focus_df['status'], rotation=45, ha='right')\n",
    "            axes[1,0].set_ylabel('Engagement Rate')\n",
    "            axes[1,0].set_title(f\"{focus_show}'s Engagement by Topic Status\\n(% of topics in each category)\")\n",
    "            axes[1,0].set_ylim([0, 1])\n",
    "            axes[1,0].grid(True, alpha=0.3, axis='y')\n",
    "            \n",
    "            # Add value labels\n",
    "            for i, row in focus_df.iterrows():\n",
    "                axes[1,0].text(i, row['rate'] + 0.02, \n",
    "                              f\"{row['n_engaged']}/{row['n_total']}\\n({row['rate']:.0%})\",\n",
    "                              ha='center', va='bottom', fontsize=9)\n",
    "    \n",
    "    # 4. Comparison: focus show vs corpus average for emerging topics\n",
    "    if focus_show in post_df.index and emerging_post_cols:\n",
    "        # Get focus show's top 15 emerging topics\n",
    "        focus_strengths = post_df.loc[focus_show, emerging_post_cols].sort_values(ascending=False).head(15)\n",
    "        \n",
    "        # FIXED: Get corpus means for the SAME topics that are in focus_strengths\n",
    "        # Ensure we're using the exact same index\n",
    "        corpus_means = post_df[focus_strengths.index].mean()\n",
    "        \n",
    "        # Now both have the same length and index\n",
    "        x = np.arange(len(focus_strengths))\n",
    "        width = 0.35\n",
    "        \n",
    "        axes[1,1].barh(x - width/2, focus_strengths.values, width, \n",
    "                      label=focus_show, color='red', alpha=0.7)\n",
    "        axes[1,1].barh(x + width/2, corpus_means.values, width, \n",
    "                      label='Corpus Average', color='steelblue', alpha=0.7)\n",
    "        \n",
    "        axes[1,1].set_yticks(x)\n",
    "        \n",
    "        # Create labels showing topic pairs\n",
    "        topic_labels = []\n",
    "        for col in focus_strengths.index:\n",
    "            # Find the topic pair that corresponds to this post column\n",
    "            pair_found = False\n",
    "            for idx, row in emerging_topics.iterrows():\n",
    "                if pd.notna(row['post_topic']) and str(row['post_topic']) == col:\n",
    "                    topic_labels.append(idx)\n",
    "                    pair_found = True\n",
    "                    break\n",
    "            if not pair_found:\n",
    "                topic_labels.append(f\"Topic {col}\")\n",
    "        \n",
    "        axes[1,1].set_yticklabels(topic_labels, fontsize=8)\n",
    "        axes[1,1].set_xlabel('Topic Strength')\n",
    "        axes[1,1].set_title(f\"{focus_show} vs Average: Top 15 Emerging Topics\")\n",
    "        axes[1,1].legend()\n",
    "        axes[1,1].invert_yaxis()\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'{focus_show.replace(\" \", \"_\")}_emergence_analysis.png', dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "def show_topic_details_aligned(comparison_df, status_filter=None, top_n=10):\n",
    "    \"\"\"\n",
    "    Print detailed information about aligned topics in a specific status category.\n",
    "    \"\"\"\n",
    "    \n",
    "    if status_filter:\n",
    "        topics = comparison_df[comparison_df['status'] == status_filter].head(top_n)\n",
    "        print(f\"\\n{'=' * 80}\")\n",
    "        print(f\"DETAILED VIEW: {status_filter.upper()} TOPICS (Top {top_n})\")\n",
    "        print(f\"{'=' * 80}\\n\")\n",
    "    else:\n",
    "        topics = comparison_df.head(top_n)\n",
    "        print(f\"\\n{'=' * 80}\")\n",
    "        print(f\"DETAILED VIEW: ALL TOPICS (Top {top_n} by engagement change)\")\n",
    "        print(f\"{'=' * 80}\\n\")\n",
    "    \n",
    "    for topic_pair, row in topics.iterrows():\n",
    "        print(f\"Topic Pair: {topic_pair}\")\n",
    "        if pd.notna(row['pre_topic']) and pd.notna(row['post_topic']):\n",
    "            print(f\"  Pre-Topic: {row['pre_topic']} → Post-Topic: {row['post_topic']}\")\n",
    "            print(f\"  Alignment similarity: {row['alignment_similarity']:.3f}\")\n",
    "        elif pd.notna(row['post_topic']):\n",
    "            print(f\"  Post-Topic: {row['post_topic']} (EMERGED - no pre-period match)\")\n",
    "        elif pd.notna(row['pre_topic']):\n",
    "            print(f\"  Pre-Topic: {row['pre_topic']} (DISAPPEARED - no post-period match)\")\n",
    "        \n",
    "        print(f\"  Status: {row['status']}\")\n",
    "        print(f\"  Engagement Rate: {row['pre_engagement_rate']:.1%} → {row['post_engagement_rate']:.1%} \"\n",
    "              f\"(change: {row['engagement_rate_change']:+.1%})\")\n",
    "        print(f\"  Shows Engaged: {row['pre_shows_engaged']:.0f} → {row['post_shows_engaged']:.0f}\")\n",
    "        print(f\"  Mean Strength (all shows): {row['pre_mean_all_shows']:.4f} → {row['post_mean_all_shows']:.4f}\")\n",
    "        print(f\"  Mean Strength (engaged shows only): {row['pre_strength_if_engaged']:.4f} → {row['post_strength_if_engaged']:.4f}\")\n",
    "        print()\n",
    "\n",
    "\n",
    "# Updated main analysis function\n",
    "def run_engagement_rate_analysis_with_alignment(pre_df, post_df, alignment_results,\n",
    "                                               threshold=0.01, \n",
    "                                               engagement_change_threshold=0.10,\n",
    "                                               show_details=True,\n",
    "                                               focus_show='Squid Game'):\n",
    "    \"\"\"\n",
    "    Run complete engagement rate analysis using topic alignment results.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Analyze engagement rates with alignment\n",
    "    comparison = analyze_topic_engagement_rates_with_alignment(\n",
    "        pre_df, post_df, alignment_results,\n",
    "        threshold=threshold,\n",
    "        engagement_change_threshold=engagement_change_threshold\n",
    "    )\n",
    "    \n",
    "    if comparison is None:\n",
    "        print(\"ERROR: Could not create comparison dataframe\")\n",
    "        return None\n",
    "    \n",
    "    # Create visualizations (reuse existing visualization function)\n",
    "    #visualize_engagement_changes(comparison, top_n=15)\n",
    "    \n",
    "    # Analyze focus show's role\n",
    "    if focus_show:\n",
    "        show_analysis = analyze_show_engagement_patterns_aligned(\n",
    "            comparison, pre_df, post_df, \n",
    "            focus_show=focus_show, \n",
    "            threshold=threshold\n",
    "        )\n",
    "        \n",
    "        # Create focus show visualizations\n",
    "        visualize_show_emergence_role(\n",
    "            comparison, post_df, \n",
    "            focus_show=focus_show, \n",
    "            threshold=threshold\n",
    "        )\n",
    "    \n",
    "    # Show details for each status category\n",
    "    if show_details:\n",
    "        for status in ['emerged', 'strengthened', 'weakened', 'disappeared']:\n",
    "            if (comparison['status'] == status).any():\n",
    "                show_topic_details_aligned(comparison, status_filter=status, top_n=10)\n",
    "    \n",
    "    return comparison\n",
    "\n",
    "\n",
    "print(\"\\nAligned Engagement Rate Analysis Ready!\")\n",
    "print(\"\\nAll functions updated to handle string column names correctly.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1011f442-3026-4990-918a-c0e318b9792a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting topic alignment analysis...\n",
      "Pre-period topics: 74\n",
      "Post-period topics: 74\n",
      "Similarity method: jaccard\n",
      "Alignment threshold: 0.2\n",
      "\n",
      "Calculating topic similarity matrix...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding best topic alignments...\n",
      "\n",
      "Alignment Results:\n",
      "Successful alignments: 121\n",
      "Unmatched pre-topics: 6\n",
      "Unmatched post-topics: 7 (potentially NEW topics)\n",
      "============================================================\n",
      "TOPIC ALIGNMENT ANALYSIS\n",
      "============================================================\n",
      "\n",
      "Found 121 topic alignments:\n",
      "Pre-Topic  Post-Topic  Similarity Quality Comparison\n",
      "----------------------------------------------------------------------\n",
      "65         69          0.905      \n",
      "5          21          0.818      \n",
      "13         38          0.818      \n",
      "26         27          0.818      \n",
      "49         45          0.818      \n",
      "4          16          0.739      \n",
      "55         26          0.739      \n",
      "22         74          0.667      \n",
      "27         22          0.667      \n",
      "59         49          0.667      \n",
      "63         4           0.667      \n",
      "2          53          0.600      \n",
      "12         35          0.600      \n",
      "14         13          0.600      \n",
      "19         23          0.600      \n",
      "29         17          0.600      \n",
      "41         47          0.600      \n",
      "48         28          0.600      \n",
      "54         14          0.600      \n",
      "57         51          0.600      \n",
      "33         56          0.560      \n",
      "23         46          0.538      \n",
      "30         36          0.538      \n",
      "35         67          0.538      \n",
      "42         48          0.538      \n",
      "46         73          0.538      \n",
      "66         59          0.538      \n",
      "3          10          0.481      \n",
      "16         7           0.481      \n",
      "18         19          0.481      \n",
      "\n",
      "Alignment Quality Statistics:\n",
      "Mean similarity: 0.381\n",
      "Median similarity: 0.300\n",
      "Min similarity: 0.212\n",
      "Max similarity: 0.905\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "POTENTIALLY NEW TOPICS (post-September):\n",
      "Topic 11\n",
      "Topic 15\n",
      "Topic 20\n",
      "Topic 32\n",
      "Topic 37\n",
      "Topic 62\n",
      "Topic 66\n",
      "\n",
      "DISAPPEARED/WEAKENED TOPICS (pre-September only):\n",
      "Topic 6\n",
      "Topic 36\n",
      "Topic 38\n",
      "Topic 43\n",
      "Topic 61\n",
      "Topic 68\n",
      "================================================================================\n",
      "TOPIC ENGAGEMENT RATE ANALYSIS (WITH ALIGNMENT)\n",
      "================================================================================\n",
      "Pre-period shows:  40\n",
      "Post-period shows: 21\n",
      "Aligned topic pairs: 121\n",
      "Unmatched pre-topics (disappeared): 6\n",
      "Unmatched post-topics (emerged): 7\n",
      "Engagement threshold: 0.013\n",
      "Change threshold for classification: 10.00%\n",
      "\n",
      "✓ Successfully processed 121 aligned topic pairs\n",
      "✓ Added 7 emerged topics\n",
      "✓ Added 6 disappeared topics\n",
      "\n",
      "================================================================================\n",
      "TOPIC STATUS SUMMARY\n",
      "================================================================================\n",
      "Emerged        :  7 topics (  5.2%)\n",
      "Strengthened   : 21 topics ( 15.7%)\n",
      "Stable         : 62 topics ( 46.3%)\n",
      "Weakened       : 38 topics ( 28.4%)\n",
      "Disappeared    :  6 topics (  4.5%)\n",
      "\n",
      "\n",
      "================================================================================\n",
      "SHOW-LEVEL ENGAGEMENT ANALYSIS: Squid Game\n",
      "================================================================================\n",
      "Found 128 post-period topics to analyze\n",
      "\n",
      "Squid Game engages with 77 topics (>0.013 threshold)\n",
      "\n",
      "\n",
      "Squid Game's Engagement by Topic Status:\n",
      "--------------------------------------------------------------------------------\n",
      "Status               Topics     Engaged    Rate         Mean Strength\n",
      "--------------------------------------------------------------------------------\n",
      "Emerged              7          2          28.6%       0.0153\n",
      "Strengthened         21         16         76.2%       0.0147\n",
      "Stable               62         43         69.4%       0.0141\n",
      "Weakened             38         16         42.1%       0.0124\n",
      "Disappeared          6          0          0.0%        0.0000\n",
      "\n",
      "\n",
      "================================================================================\n",
      "TOP EMERGING/STRENGTHENED TOPICS FOR Squid Game\n",
      "================================================================================\n",
      "\n",
      "Squid Game engages with 18 out of 28 emerging/strengthened topics:\n",
      "\n",
      " 1. NEW->66\n",
      "    Status: Emerged (+4.8% engagement rate change)\n",
      "    Squid Game strength: 0.0533\n",
      "    Overall post-period engagement: 4.8% of shows\n",
      "\n",
      " 2. 46->73\n",
      "    Status: Strengthened (+17.7% engagement rate change)\n",
      "    Squid Game strength: 0.0222\n",
      "    Overall post-period engagement: 95.2% of shows\n",
      "\n",
      " 3. 56->57\n",
      "    Status: Strengthened (+42.7% engagement rate change)\n",
      "    Squid Game strength: 0.0204\n",
      "    Overall post-period engagement: 95.2% of shows\n",
      "\n",
      " 4. 1->28\n",
      "    Status: Strengthened (+15.0% engagement rate change)\n",
      "    Squid Game strength: 0.0193\n",
      "    Overall post-period engagement: 100.0% of shows\n",
      "\n",
      " 5. 1->34\n",
      "    Status: Strengthened (+15.0% engagement rate change)\n",
      "    Squid Game strength: 0.0186\n",
      "    Overall post-period engagement: 100.0% of shows\n",
      "\n",
      " 6. 14->13\n",
      "    Status: Strengthened (+36.9% engagement rate change)\n",
      "    Squid Game strength: 0.0175\n",
      "    Overall post-period engagement: 61.9% of shows\n",
      "\n",
      " 7. 44->64\n",
      "    Status: Strengthened (+85.2% engagement rate change)\n",
      "    Squid Game strength: 0.0173\n",
      "    Overall post-period engagement: 95.2% of shows\n",
      "\n",
      " 8. 34->25\n",
      "    Status: Strengthened (+67.5% engagement rate change)\n",
      "    Squid Game strength: 0.0161\n",
      "    Overall post-period engagement: 100.0% of shows\n",
      "\n",
      " 9. 8->16\n",
      "    Status: Strengthened (+61.7% engagement rate change)\n",
      "    Squid Game strength: 0.0156\n",
      "    Overall post-period engagement: 66.7% of shows\n",
      "\n",
      "10. NEW->37\n",
      "    Status: Emerged (+33.3% engagement rate change)\n",
      "    Squid Game strength: 0.0155\n",
      "    Overall post-period engagement: 33.3% of shows\n",
      "\n",
      "11. 21->65\n",
      "    Status: Strengthened (+46.9% engagement rate change)\n",
      "    Squid Game strength: 0.0146\n",
      "    Overall post-period engagement: 61.9% of shows\n",
      "\n",
      "12. 28->65\n",
      "    Status: Strengthened (+51.9% engagement rate change)\n",
      "    Squid Game strength: 0.0146\n",
      "    Overall post-period engagement: 61.9% of shows\n",
      "\n",
      "13. 10->27\n",
      "    Status: Strengthened (+18.7% engagement rate change)\n",
      "    Squid Game strength: 0.0133\n",
      "    Overall post-period engagement: 76.2% of shows\n",
      "\n",
      "14. 14->72\n",
      "    Status: Strengthened (+60.7% engagement rate change)\n",
      "    Squid Game strength: 0.0130\n",
      "    Overall post-period engagement: 85.7% of shows\n",
      "\n",
      "15. 73->8\n",
      "    Status: Strengthened (+42.1% engagement rate change)\n",
      "    Squid Game strength: 0.0129\n",
      "    Overall post-period engagement: 57.1% of shows\n",
      "\n",
      "\n",
      "================================================================================\n",
      "Squid Game'S RANK IN EMERGING/STRENGTHENED TOPICS\n",
      "================================================================================\n",
      "\n",
      "Topics where Squid Game ranks highest:\n",
      "\n",
      "NEW->66 (Post-Topic 66)\n",
      "  Rank: #1 out of 21 shows (strength: 0.0533)\n",
      "  >>> Squid Game is #1 for this topic! <<<\n",
      "\n",
      "46->73 (Post-Topic 73)\n",
      "  Rank: #2 out of 21 shows (strength: 0.0222)\n",
      "  Top show: The Handmaid's Tale (strength: 0.0228)\n",
      "\n",
      "44->64 (Post-Topic 64)\n",
      "  Rank: #3 out of 21 shows (strength: 0.0173)\n",
      "  Top show: Attack on Titan (strength: 0.0195)\n",
      "\n",
      "1->28 (Post-Topic 28)\n",
      "  Rank: #4 out of 21 shows (strength: 0.0193)\n",
      "  Top show: Better Call Saul (strength: 0.0207)\n",
      "\n",
      "56->57 (Post-Topic 57)\n",
      "  Rank: #4 out of 21 shows (strength: 0.0204)\n",
      "  Top show: Cobra Kai (strength: 0.0231)\n",
      "\n",
      "NEW->37 (Post-Topic 37)\n",
      "  Rank: #4 out of 21 shows (strength: 0.0155)\n",
      "  Top show: Better Call Saul (strength: 0.0441)\n",
      "\n",
      "34->25 (Post-Topic 25)\n",
      "  Rank: #6 out of 21 shows (strength: 0.0161)\n",
      "  Top show: The Umbrella Academy (strength: 0.0174)\n",
      "\n",
      "1->34 (Post-Topic 34)\n",
      "  Rank: #7 out of 21 shows (strength: 0.0186)\n",
      "  Top show: Moon Knight (strength: 0.0225)\n",
      "\n",
      "14->13 (Post-Topic 13)\n",
      "  Rank: #7 out of 21 shows (strength: 0.0175)\n",
      "  Top show: The Boys (strength: 0.0230)\n",
      "\n",
      "21->65 (Post-Topic 65)\n",
      "  Rank: #8 out of 21 shows (strength: 0.0146)\n",
      "  Top show: The Mandalorian (strength: 0.0240)\n",
      "\n",
      "\n",
      "================================================================================\n",
      "Squid Game LEADERSHIP SUMMARY\n",
      "================================================================================\n",
      "Topics ranked in top 5:  6 out of 28\n",
      "Topics ranked in top 10: 13 out of 28\n",
      "Median rank across emerging/strengthened topics: 11.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================\n",
      "DETAILED VIEW: EMERGED TOPICS (Top 10)\n",
      "================================================================================\n",
      "\n",
      "Topic Pair: NEW->62\n",
      "  Post-Topic: 62 (EMERGED - no pre-period match)\n",
      "  Status: emerged\n",
      "  Engagement Rate: 0.0% → 33.3% (change: +33.3%)\n",
      "  Shows Engaged: 0 → 7\n",
      "  Mean Strength (all shows): 0.0000 → 0.0125\n",
      "  Mean Strength (engaged shows only): 0.0000 → 0.0187\n",
      "\n",
      "Topic Pair: NEW->37\n",
      "  Post-Topic: 37 (EMERGED - no pre-period match)\n",
      "  Status: emerged\n",
      "  Engagement Rate: 0.0% → 33.3% (change: +33.3%)\n",
      "  Shows Engaged: 0 → 7\n",
      "  Mean Strength (all shows): 0.0000 → 0.0129\n",
      "  Mean Strength (engaged shows only): 0.0000 → 0.0195\n",
      "\n",
      "Topic Pair: NEW->11\n",
      "  Post-Topic: 11 (EMERGED - no pre-period match)\n",
      "  Status: emerged\n",
      "  Engagement Rate: 0.0% → 23.8% (change: +23.8%)\n",
      "  Shows Engaged: 0 → 5\n",
      "  Mean Strength (all shows): 0.0000 → 0.0113\n",
      "  Mean Strength (engaged shows only): 0.0000 → 0.0163\n",
      "\n",
      "Topic Pair: NEW->32\n",
      "  Post-Topic: 32 (EMERGED - no pre-period match)\n",
      "  Status: emerged\n",
      "  Engagement Rate: 0.0% → 9.5% (change: +9.5%)\n",
      "  Shows Engaged: 0 → 2\n",
      "  Mean Strength (all shows): 0.0000 → 0.0098\n",
      "  Mean Strength (engaged shows only): 0.0000 → 0.0275\n",
      "\n",
      "Topic Pair: NEW->20\n",
      "  Post-Topic: 20 (EMERGED - no pre-period match)\n",
      "  Status: emerged\n",
      "  Engagement Rate: 0.0% → 9.5% (change: +9.5%)\n",
      "  Shows Engaged: 0 → 2\n",
      "  Mean Strength (all shows): 0.0000 → 0.0115\n",
      "  Mean Strength (engaged shows only): 0.0000 → 0.0495\n",
      "\n",
      "Topic Pair: NEW->15\n",
      "  Post-Topic: 15 (EMERGED - no pre-period match)\n",
      "  Status: emerged\n",
      "  Engagement Rate: 0.0% → 9.5% (change: +9.5%)\n",
      "  Shows Engaged: 0 → 2\n",
      "  Mean Strength (all shows): 0.0000 → 0.0096\n",
      "  Mean Strength (engaged shows only): 0.0000 → 0.0324\n",
      "\n",
      "Topic Pair: NEW->66\n",
      "  Post-Topic: 66 (EMERGED - no pre-period match)\n",
      "  Status: emerged\n",
      "  Engagement Rate: 0.0% → 4.8% (change: +4.8%)\n",
      "  Shows Engaged: 0 → 1\n",
      "  Mean Strength (all shows): 0.0000 → 0.0110\n",
      "  Mean Strength (engaged shows only): 0.0000 → 0.0533\n",
      "\n",
      "\n",
      "================================================================================\n",
      "DETAILED VIEW: STRENGTHENED TOPICS (Top 10)\n",
      "================================================================================\n",
      "\n",
      "Topic Pair: 31->51\n",
      "  Pre-Topic: 31 → Post-Topic: 51\n",
      "  Alignment similarity: 0.212\n",
      "  Status: strengthened\n",
      "  Engagement Rate: 0.0% → 90.5% (change: +90.5%)\n",
      "  Shows Engaged: 0 → 19\n",
      "  Mean Strength (all shows): 0.0061 → 0.0169\n",
      "  Mean Strength (engaged shows only): 0.0000 → 0.0174\n",
      "\n",
      "Topic Pair: 44->64\n",
      "  Pre-Topic: 44 → Post-Topic: 64\n",
      "  Alignment similarity: 0.212\n",
      "  Status: strengthened\n",
      "  Engagement Rate: 10.0% → 95.2% (change: +85.2%)\n",
      "  Shows Engaged: 4 → 20\n",
      "  Mean Strength (all shows): 0.0093 → 0.0161\n",
      "  Mean Strength (engaged shows only): 0.0136 → 0.0163\n",
      "\n",
      "Topic Pair: 34->25\n",
      "  Pre-Topic: 34 → Post-Topic: 25\n",
      "  Alignment similarity: 0.212\n",
      "  Status: strengthened\n",
      "  Engagement Rate: 32.5% → 100.0% (change: +67.5%)\n",
      "  Shows Engaged: 13 → 21\n",
      "  Mean Strength (all shows): 0.0121 → 0.0155\n",
      "  Mean Strength (engaged shows only): 0.0154 → 0.0155\n",
      "\n",
      "Topic Pair: 8->16\n",
      "  Pre-Topic: 8 → Post-Topic: 16\n",
      "  Alignment similarity: 0.212\n",
      "  Status: strengthened\n",
      "  Engagement Rate: 5.0% → 66.7% (change: +61.7%)\n",
      "  Shows Engaged: 2 → 14\n",
      "  Mean Strength (all shows): 0.0103 → 0.0164\n",
      "  Mean Strength (engaged shows only): 0.0502 → 0.0194\n",
      "\n",
      "Topic Pair: 14->72\n",
      "  Pre-Topic: 14 → Post-Topic: 72\n",
      "  Alignment similarity: 0.250\n",
      "  Status: strengthened\n",
      "  Engagement Rate: 25.0% → 85.7% (change: +60.7%)\n",
      "  Shows Engaged: 10 → 18\n",
      "  Mean Strength (all shows): 0.0117 → 0.0154\n",
      "  Mean Strength (engaged shows only): 0.0162 → 0.0159\n",
      "\n",
      "Topic Pair: 28->65\n",
      "  Pre-Topic: 28 → Post-Topic: 65\n",
      "  Alignment similarity: 0.250\n",
      "  Status: strengthened\n",
      "  Engagement Rate: 10.0% → 61.9% (change: +51.9%)\n",
      "  Shows Engaged: 4 → 13\n",
      "  Mean Strength (all shows): 0.0096 → 0.0146\n",
      "  Mean Strength (engaged shows only): 0.0146 → 0.0164\n",
      "\n",
      "Topic Pair: 21->65\n",
      "  Pre-Topic: 21 → Post-Topic: 65\n",
      "  Alignment similarity: 0.481\n",
      "  Status: strengthened\n",
      "  Engagement Rate: 15.0% → 61.9% (change: +46.9%)\n",
      "  Shows Engaged: 6 → 13\n",
      "  Mean Strength (all shows): 0.0106 → 0.0146\n",
      "  Mean Strength (engaged shows only): 0.0150 → 0.0164\n",
      "\n",
      "Topic Pair: 15->61\n",
      "  Pre-Topic: 15 → Post-Topic: 61\n",
      "  Alignment similarity: 0.250\n",
      "  Status: strengthened\n",
      "  Engagement Rate: 22.5% → 66.7% (change: +44.2%)\n",
      "  Shows Engaged: 9 → 14\n",
      "  Mean Strength (all shows): 0.0129 → 0.0140\n",
      "  Mean Strength (engaged shows only): 0.0265 → 0.0153\n",
      "\n",
      "Topic Pair: 56->57\n",
      "  Pre-Topic: 56 → Post-Topic: 57\n",
      "  Alignment similarity: 0.290\n",
      "  Status: strengthened\n",
      "  Engagement Rate: 52.5% → 95.2% (change: +42.7%)\n",
      "  Shows Engaged: 21 → 20\n",
      "  Mean Strength (all shows): 0.0131 → 0.0173\n",
      "  Mean Strength (engaged shows only): 0.0152 → 0.0176\n",
      "\n",
      "Topic Pair: 73->8\n",
      "  Pre-Topic: 73 → Post-Topic: 8\n",
      "  Alignment similarity: 0.290\n",
      "  Status: strengthened\n",
      "  Engagement Rate: 15.0% → 57.1% (change: +42.1%)\n",
      "  Shows Engaged: 6 → 12\n",
      "  Mean Strength (all shows): 0.0107 → 0.0142\n",
      "  Mean Strength (engaged shows only): 0.0145 → 0.0165\n",
      "\n",
      "\n",
      "================================================================================\n",
      "DETAILED VIEW: WEAKENED TOPICS (Top 10)\n",
      "================================================================================\n",
      "\n",
      "Topic Pair: 13->38\n",
      "  Pre-Topic: 13 → Post-Topic: 38\n",
      "  Alignment similarity: 0.818\n",
      "  Status: weakened\n",
      "  Engagement Rate: 15.0% → 4.8% (change: -10.2%)\n",
      "  Shows Engaged: 6 → 1\n",
      "  Mean Strength (all shows): 0.0092 → 0.0083\n",
      "  Mean Strength (engaged shows only): 0.0147 → 0.0188\n",
      "\n",
      "Topic Pair: 42->40\n",
      "  Pre-Topic: 42 → Post-Topic: 40\n",
      "  Alignment similarity: 0.212\n",
      "  Status: weakened\n",
      "  Engagement Rate: 40.0% → 28.6% (change: -11.4%)\n",
      "  Shows Engaged: 16 → 6\n",
      "  Mean Strength (all shows): 0.0134 → 0.0122\n",
      "  Mean Strength (engaged shows only): 0.0170 → 0.0142\n",
      "\n",
      "Topic Pair: 35->67\n",
      "  Pre-Topic: 35 → Post-Topic: 67\n",
      "  Alignment similarity: 0.538\n",
      "  Status: weakened\n",
      "  Engagement Rate: 22.5% → 9.5% (change: -13.0%)\n",
      "  Shows Engaged: 9 → 2\n",
      "  Mean Strength (all shows): 0.0110 → 0.0103\n",
      "  Mean Strength (engaged shows only): 0.0146 → 0.0198\n",
      "\n",
      "Topic Pair: 25->31\n",
      "  Pre-Topic: 25 → Post-Topic: 31\n",
      "  Alignment similarity: 0.481\n",
      "  Status: weakened\n",
      "  Engagement Rate: 75.0% → 61.9% (change: -13.1%)\n",
      "  Shows Engaged: 30 → 13\n",
      "  Mean Strength (all shows): 0.0148 → 0.0139\n",
      "  Mean Strength (engaged shows only): 0.0160 → 0.0155\n",
      "\n",
      "Topic Pair: 71->72\n",
      "  Pre-Topic: 71 → Post-Topic: 72\n",
      "  Alignment similarity: 0.481\n",
      "  Status: weakened\n",
      "  Engagement Rate: 100.0% → 85.7% (change: -14.3%)\n",
      "  Shows Engaged: 40 → 18\n",
      "  Mean Strength (all shows): 0.0186 → 0.0154\n",
      "  Mean Strength (engaged shows only): 0.0186 → 0.0159\n",
      "\n",
      "Topic Pair: 64->33\n",
      "  Pre-Topic: 64 → Post-Topic: 33\n",
      "  Alignment similarity: 0.379\n",
      "  Status: weakened\n",
      "  Engagement Rate: 30.0% → 14.3% (change: -15.7%)\n",
      "  Shows Engaged: 12 → 3\n",
      "  Mean Strength (all shows): 0.0119 → 0.0102\n",
      "  Mean Strength (engaged shows only): 0.0169 → 0.0145\n",
      "\n",
      "Topic Pair: 64->1\n",
      "  Pre-Topic: 64 → Post-Topic: 1\n",
      "  Alignment similarity: 0.429\n",
      "  Status: weakened\n",
      "  Engagement Rate: 30.0% → 14.3% (change: -15.7%)\n",
      "  Shows Engaged: 12 → 3\n",
      "  Mean Strength (all shows): 0.0119 → 0.0097\n",
      "  Mean Strength (engaged shows only): 0.0169 → 0.0187\n",
      "\n",
      "Topic Pair: 42->48\n",
      "  Pre-Topic: 42 → Post-Topic: 48\n",
      "  Alignment similarity: 0.538\n",
      "  Status: weakened\n",
      "  Engagement Rate: 40.0% → 23.8% (change: -16.2%)\n",
      "  Shows Engaged: 16 → 5\n",
      "  Mean Strength (all shows): 0.0134 → 0.0115\n",
      "  Mean Strength (engaged shows only): 0.0170 → 0.0181\n",
      "\n",
      "Topic Pair: 66->58\n",
      "  Pre-Topic: 66 → Post-Topic: 58\n",
      "  Alignment similarity: 0.290\n",
      "  Status: weakened\n",
      "  Engagement Rate: 65.0% → 47.6% (change: -17.4%)\n",
      "  Shows Engaged: 26 → 10\n",
      "  Mean Strength (all shows): 0.0147 → 0.0139\n",
      "  Mean Strength (engaged shows only): 0.0164 → 0.0175\n",
      "\n",
      "Topic Pair: 27->71\n",
      "  Pre-Topic: 27 → Post-Topic: 71\n",
      "  Alignment similarity: 0.212\n",
      "  Status: weakened\n",
      "  Engagement Rate: 70.0% → 52.4% (change: -17.6%)\n",
      "  Shows Engaged: 28 → 11\n",
      "  Mean Strength (all shows): 0.0156 → 0.0138\n",
      "  Mean Strength (engaged shows only): 0.0174 → 0.0169\n",
      "\n",
      "\n",
      "================================================================================\n",
      "DETAILED VIEW: DISAPPEARED TOPICS (Top 10)\n",
      "================================================================================\n",
      "\n",
      "Topic Pair: 6->GONE\n",
      "  Pre-Topic: 6 (DISAPPEARED - no post-period match)\n",
      "  Status: disappeared\n",
      "  Engagement Rate: 2.5% → 0.0% (change: -2.5%)\n",
      "  Shows Engaged: 1 → 0\n",
      "  Mean Strength (all shows): 0.0077 → 0.0000\n",
      "  Mean Strength (engaged shows only): 0.0248 → 0.0000\n",
      "\n",
      "Topic Pair: 38->GONE\n",
      "  Pre-Topic: 38 (DISAPPEARED - no post-period match)\n",
      "  Status: disappeared\n",
      "  Engagement Rate: 5.0% → 0.0% (change: -5.0%)\n",
      "  Shows Engaged: 2 → 0\n",
      "  Mean Strength (all shows): 0.0090 → 0.0000\n",
      "  Mean Strength (engaged shows only): 0.0305 → 0.0000\n",
      "\n",
      "Topic Pair: 68->GONE\n",
      "  Pre-Topic: 68 (DISAPPEARED - no post-period match)\n",
      "  Status: disappeared\n",
      "  Engagement Rate: 5.0% → 0.0% (change: -5.0%)\n",
      "  Shows Engaged: 2 → 0\n",
      "  Mean Strength (all shows): 0.0084 → 0.0000\n",
      "  Mean Strength (engaged shows only): 0.0243 → 0.0000\n",
      "\n",
      "Topic Pair: 36->GONE\n",
      "  Pre-Topic: 36 (DISAPPEARED - no post-period match)\n",
      "  Status: disappeared\n",
      "  Engagement Rate: 7.5% → 0.0% (change: -7.5%)\n",
      "  Shows Engaged: 3 → 0\n",
      "  Mean Strength (all shows): 0.0081 → 0.0000\n",
      "  Mean Strength (engaged shows only): 0.0209 → 0.0000\n",
      "\n",
      "Topic Pair: 43->GONE\n",
      "  Pre-Topic: 43 (DISAPPEARED - no post-period match)\n",
      "  Status: disappeared\n",
      "  Engagement Rate: 15.0% → 0.0% (change: -15.0%)\n",
      "  Shows Engaged: 6 → 0\n",
      "  Mean Strength (all shows): 0.0090 → 0.0000\n",
      "  Mean Strength (engaged shows only): 0.0164 → 0.0000\n",
      "\n",
      "Topic Pair: 61->GONE\n",
      "  Pre-Topic: 61 (DISAPPEARED - no post-period match)\n",
      "  Status: disappeared\n",
      "  Engagement Rate: 27.5% → 0.0% (change: -27.5%)\n",
      "  Shows Engaged: 11 → 0\n",
      "  Mean Strength (all shows): 0.0101 → 0.0000\n",
      "  Mean Strength (engaged shows only): 0.0143 → 0.0000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# First, run topic alignment:\n",
    "alignment_results = run_topic_alignment_analysis(pre_words, post_words)\n",
    "\n",
    "# Then run engagement analysis with alignment:\n",
    "comparison = run_engagement_rate_analysis_with_alignment(\n",
    "    pre_df,\n",
    "    post_df,\n",
    "    alignment_results,  # Uses alignment to match topics\n",
    "    threshold=0.0127,  #based on analysis in previous cells\n",
    "    engagement_change_threshold=0.10,\n",
    "    show_details=True,\n",
    "    focus_show='Squid Game'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aea29832-201e-47ae-8d05-302355ce5cca",
   "metadata": {},
   "source": [
    "### Analyze evolution of specific topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a0934f8c-97d6-4244-9c44-76e355f54af9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Usage:\n",
      "money_analysis = run_topic_evolution_analysis(\n",
      "    pre_topics_filtered,\n",
      "    post_topics_filtered,\n",
      "    pre_topic='topic_34',  # Money topic in pre-period\n",
      "    post_topic='topic_18',  # Money topic in post-period\n",
      "    topic_name='Money',\n",
      "    threshold=0.01,\n",
      "    focus_show='Squid Game'\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "def analyze_specific_topic_evolution(pre_df, post_df, pre_topic, post_topic, \n",
    "                                     topic_name=\"Money\", threshold=0.01,\n",
    "                                     focus_show='Squid Game'):\n",
    "    \"\"\"\n",
    "    Detailed analysis of how a specific topic evolved between pre and post periods.\n",
    "    Now includes percentage share calculations.\n",
    "    \n",
    "    Parameters:\n",
    "    -----------\n",
    "    pre_df : DataFrame\n",
    "        Pre-period topic proportions (shows x topics)\n",
    "    post_df : DataFrame\n",
    "        Post-period topic proportions (shows x topics)\n",
    "    pre_topic : str\n",
    "        Topic column name in pre_df (e.g., '34')\n",
    "    post_topic : str\n",
    "        Topic column name in post_df (e.g., '18')\n",
    "    topic_name : str\n",
    "        Human-readable name for the topic (e.g., \"Money\")\n",
    "    threshold : float\n",
    "        Engagement threshold\n",
    "    focus_show : str\n",
    "        Show to highlight in analysis\n",
    "    \"\"\"\n",
    "    \n",
    "    print(\"=\" * 80)\n",
    "    print(f\"{topic_name.upper()} TOPIC EVOLUTION ANALYSIS\")\n",
    "    print(\"=\" * 80)\n",
    "    print(f\"Pre-period topic:  {pre_topic}\")\n",
    "    print(f\"Post-period topic: {post_topic}\")\n",
    "    print(f\"Engagement threshold: {threshold:.3f}\")\n",
    "    print()\n",
    "    \n",
    "    # Get topic data (ensure string columns)\n",
    "    pre_col = str(pre_topic)\n",
    "    post_col = str(post_topic)\n",
    "    \n",
    "    pre_money = pre_df[pre_col] if pre_col in pre_df.columns else pd.Series(0, index=pre_df.index)\n",
    "    post_money = post_df[post_col] if post_col in post_df.columns else pd.Series(0, index=post_df.index)\n",
    "    \n",
    "    # Calculate total engagement for percentage shares\n",
    "    pre_total = pre_money.sum()\n",
    "    post_total = post_money.sum()\n",
    "    \n",
    "    # Overall metrics\n",
    "    pre_mean = pre_money.mean()\n",
    "    post_mean = post_money.mean()\n",
    "    pre_engaged = (pre_money > threshold).sum()\n",
    "    post_engaged = (post_money > threshold).sum()\n",
    "    pre_engaged_rate = pre_engaged / len(pre_df)\n",
    "    post_engaged_rate = post_engaged / len(post_df)\n",
    "    \n",
    "    print(\"=\" * 80)\n",
    "    print(\"OVERALL TOPIC METRICS\")\n",
    "    print(\"=\" * 80)\n",
    "    print(f\"Total engagement:  {pre_total:.4f} → {post_total:.4f} \"\n",
    "          f\"(change: {post_total - pre_total:+.4f})\")\n",
    "    print(f\"Mean strength:     {pre_mean:.4f} → {post_mean:.4f} \"\n",
    "          f\"(change: {post_mean - pre_mean:+.4f})\")\n",
    "    print(f\"Shows engaged:     {pre_engaged} → {post_engaged} \"\n",
    "          f\"({post_engaged - pre_engaged:+d} shows)\")\n",
    "    print(f\"Engagement rate:   {pre_engaged_rate:.1%} → {post_engaged_rate:.1%} \"\n",
    "          f\"(change: {post_engaged_rate - pre_engaged_rate:+.1%})\")\n",
    "    \n",
    "    if pre_mean > 0:\n",
    "        fold_change = post_mean / pre_mean\n",
    "        print(f\"Fold change:       {fold_change:.2f}x\")\n",
    "    print()\n",
    "    \n",
    "    # Top shows in each period WITH SHARES\n",
    "    pre_top = pre_money.sort_values(ascending=False).head(15)\n",
    "    post_top = post_money.sort_values(ascending=False).head(15)\n",
    "    \n",
    "    print(\"=\" * 80)\n",
    "    print(f\"TOP 15 SHOWS: {topic_name.upper()} TOPIC\")\n",
    "    print(\"=\" * 80)\n",
    "    \n",
    "    print(\"\\nPRE-PERIOD:\")\n",
    "    print(\"-\" * 80)\n",
    "    print(f\"{'Rank':<6} {'Show':<40} {'Engagement':<12} {'% Share'}\")\n",
    "    print(\"-\" * 80)\n",
    "    for i, (show, strength) in enumerate(pre_top.items(), 1):\n",
    "        share = (strength / pre_total * 100) if pre_total > 0 else 0\n",
    "        marker = \" ←\" if show == focus_show else \"\"\n",
    "        print(f\"{i:<6} {show:<40} {strength:<12.4f} {share:>6.2f}%{marker}\")\n",
    "    \n",
    "    print(\"\\nPOST-PERIOD:\")\n",
    "    print(\"-\" * 80)\n",
    "    print(f\"{'Rank':<6} {'Show':<40} {'Engagement':<12} {'% Share'}\")\n",
    "    print(\"-\" * 80)\n",
    "    for i, (show, strength) in enumerate(post_top.items(), 1):\n",
    "        share = (strength / post_total * 100) if post_total > 0 else 0\n",
    "        marker = \" ←\" if show == focus_show else \"\"\n",
    "        print(f\"{i:<6} {show:<40} {strength:<12.4f} {share:>6.2f}%{marker}\")\n",
    "    \n",
    "    # Focus show analysis WITH SHARES\n",
    "    if focus_show in pre_money.index or focus_show in post_money.index:\n",
    "        print()\n",
    "        print(\"=\" * 80)\n",
    "        print(f\"{focus_show.upper()} ANALYSIS\")\n",
    "        print(\"=\" * 80)\n",
    "        \n",
    "        pre_strength = pre_money.get(focus_show, 0)\n",
    "        post_strength = post_money.get(focus_show, 0)\n",
    "        \n",
    "        pre_share = (pre_strength / pre_total * 100) if pre_total > 0 else 0\n",
    "        post_share = (post_strength / post_total * 100) if post_total > 0 else 0\n",
    "        \n",
    "        if focus_show in pre_money.index:\n",
    "            pre_rank = list(pre_money.sort_values(ascending=False).index).index(focus_show) + 1\n",
    "        else:\n",
    "            pre_rank = None\n",
    "        \n",
    "        if focus_show in post_money.index:\n",
    "            post_rank = list(post_money.sort_values(ascending=False).index).index(focus_show) + 1\n",
    "        else:\n",
    "            post_rank = None\n",
    "        \n",
    "        print(f\"Strength:          {pre_strength:.4f} → {post_strength:.4f}\")\n",
    "        print(f\"Share of topic:    {pre_share:.2f}% → {post_share:.2f}%\")\n",
    "        print(f\"Rank:              {pre_rank if pre_rank else 'N/A'} → {post_rank if post_rank else 'N/A'}\")\n",
    "        \n",
    "        if pre_strength > 0 and post_strength > 0:\n",
    "            focus_fold = post_strength / pre_strength\n",
    "            share_change = post_share - pre_share\n",
    "            print(f\"Fold change:       {focus_fold:.2f}x\")\n",
    "            print(f\"Share change:      {share_change:+.2f} percentage points\")\n",
    "        elif pre_strength == 0 and post_strength > 0:\n",
    "            print(f\"Status:            NEW - emerged in post-period with {post_share:.2f}% share\")\n",
    "        print()\n",
    "    \n",
    "    # Leadership changes WITH SHARES\n",
    "    print(\"=\" * 80)\n",
    "    print(\"LEADERSHIP CHANGES\")\n",
    "    print(\"=\" * 80)\n",
    "    \n",
    "    pre_top5 = set(pre_top.head(5).index)\n",
    "    post_top5 = set(post_top.head(5).index)\n",
    "    \n",
    "    maintained = pre_top5 & post_top5\n",
    "    lost = pre_top5 - post_top5\n",
    "    gained = post_top5 - pre_top5\n",
    "    \n",
    "    print(f\"\\nMaintained top 5:  {len(maintained)} shows\")\n",
    "    if maintained:\n",
    "        for show in maintained:\n",
    "            pre_s = pre_money[show]\n",
    "            post_s = post_money[show]\n",
    "            pre_sh = (pre_s / pre_total * 100) if pre_total > 0 else 0\n",
    "            post_sh = (post_s / post_total * 100) if post_total > 0 else 0\n",
    "            print(f\"  - {show}: {pre_sh:.2f}% → {post_sh:.2f}%\")\n",
    "    \n",
    "    print(f\"\\nLost top 5:        {len(lost)} shows\")\n",
    "    if lost:\n",
    "        for show in lost:\n",
    "            pre_s = pre_money[show]\n",
    "            pre_sh = (pre_s / pre_total * 100) if pre_total > 0 else 0\n",
    "            post_s = post_money.get(show, 0)\n",
    "            post_sh = (post_s / post_total * 100) if post_total > 0 else 0\n",
    "            print(f\"  - {show}: {pre_sh:.2f}% → {post_sh:.2f}%\")\n",
    "    \n",
    "    print(f\"\\nGained top 5:      {len(gained)} shows\")\n",
    "    if gained:\n",
    "        for show in gained:\n",
    "            pre_s = pre_money.get(show, 0)\n",
    "            pre_sh = (pre_s / pre_total * 100) if pre_total > 0 else 0\n",
    "            post_s = post_money[show]\n",
    "            post_sh = (post_s / post_total * 100) if post_total > 0 else 0\n",
    "            print(f\"  - {show}: {pre_sh:.2f}% → {post_sh:.2f}%\")\n",
    "    \n",
    "    # Biggest changes WITH SHARES\n",
    "    print()\n",
    "    print(\"=\" * 80)\n",
    "    print(\"BIGGEST CHANGES\")\n",
    "    print(\"=\" * 80)\n",
    "    \n",
    "    # Get common shows\n",
    "    common_shows = set(pre_money.index) & set(post_money.index)\n",
    "    changes = pd.DataFrame({\n",
    "        'pre': pre_money[list(common_shows)],\n",
    "        'post': post_money[list(common_shows)]\n",
    "    })\n",
    "    changes['change'] = changes['post'] - changes['pre']\n",
    "    changes['pre_share'] = (changes['pre'] / pre_total * 100) if pre_total > 0 else 0\n",
    "    changes['post_share'] = (changes['post'] / post_total * 100) if post_total > 0 else 0\n",
    "    changes['share_change'] = changes['post_share'] - changes['pre_share']\n",
    "    \n",
    "    print(\"\\nTop 10 Gainers:\")\n",
    "    print(\"-\" * 80)\n",
    "    print(f\"{'Rank':<6} {'Show':<35} {'Pre %':<8} {'Post %':<8} {'Change'}\")\n",
    "    print(\"-\" * 80)\n",
    "    gainers = changes.sort_values('share_change', ascending=False).head(10)\n",
    "    for i, (show, row) in enumerate(gainers.iterrows(), 1):\n",
    "        marker = \" ←\" if show == focus_show else \"\"\n",
    "        print(f\"{i:<6} {show:<35} {row['pre_share']:<7.2f}% {row['post_share']:<7.2f}% \"\n",
    "              f\"{row['share_change']:+.2f}pp{marker}\")\n",
    "    \n",
    "    print(\"\\nTop 10 Decliners:\")\n",
    "    print(\"-\" * 80)\n",
    "    print(f\"{'Rank':<6} {'Show':<35} {'Pre %':<8} {'Post %':<8} {'Change'}\")\n",
    "    print(\"-\" * 80)\n",
    "    decliners = changes.sort_values('share_change', ascending=True).head(10)\n",
    "    for i, (show, row) in enumerate(decliners.iterrows(), 1):\n",
    "        marker = \" ←\" if show == focus_show else \"\"\n",
    "        print(f\"{i:<6} {show:<35} {row['pre_share']:<7.2f}% {row['post_share']:<7.2f}% \"\n",
    "              f\"{row['share_change']:+.2f}pp{marker}\")\n",
    "    \n",
    "    return {\n",
    "        'pre_money': pre_money,\n",
    "        'post_money': post_money,\n",
    "        'pre_total': pre_total,\n",
    "        'post_total': post_total,\n",
    "        'changes': changes,\n",
    "        'pre_top': pre_top,\n",
    "        'post_top': post_top\n",
    "    }\n",
    "\n",
    "def visualize_topic_evolution(analysis_results, topic_name=\"Money\", focus_show='Squid Game'):\n",
    "    \"\"\"\n",
    "    Create visualizations for topic evolution analysis.\n",
    "    \"\"\"\n",
    "    \n",
    "    pre_money = analysis_results['pre_money']\n",
    "    post_money = analysis_results['post_money']\n",
    "    changes = analysis_results['changes']\n",
    "    \n",
    "    fig, axes = plt.subplots(2, 2, figsize=(18, 12))\n",
    "    \n",
    "    # 1. Top shows comparison (side-by-side)\n",
    "    pre_top = pre_money.sort_values(ascending=False).head(15)\n",
    "    post_top = post_money.sort_values(ascending=False).head(15)\n",
    "    \n",
    "    # Combine and get all unique shows\n",
    "    all_top_shows = list(dict.fromkeys(list(pre_top.index) + list(post_top.index)))[:15]\n",
    "    \n",
    "    x = np.arange(len(all_top_shows))\n",
    "    width = 0.35\n",
    "    \n",
    "    pre_values = [pre_money.get(show, 0) for show in all_top_shows]\n",
    "    post_values = [post_money.get(show, 0) for show in all_top_shows]\n",
    "    \n",
    "    bars1 = axes[0,0].barh(x - width/2, pre_values, width, label='Pre-Period', \n",
    "                           color='steelblue', alpha=0.7)\n",
    "    bars2 = axes[0,0].barh(x + width/2, post_values, width, label='Post-Period', \n",
    "                           color='coral', alpha=0.7)\n",
    "    \n",
    "    # Highlight focus show\n",
    "    for i, show in enumerate(all_top_shows):\n",
    "        if show == focus_show:\n",
    "            bars1[i].set_edgecolor('red')\n",
    "            bars1[i].set_linewidth(2)\n",
    "            bars2[i].set_edgecolor('red')\n",
    "            bars2[i].set_linewidth(2)\n",
    "    \n",
    "    axes[0,0].set_yticks(x)\n",
    "    axes[0,0].set_yticklabels(all_top_shows, fontsize=9)\n",
    "    axes[0,0].set_xlabel('Topic Strength')\n",
    "    axes[0,0].set_title(f'Top Shows: {topic_name} Topic (Pre vs Post)')\n",
    "    axes[0,0].legend()\n",
    "    axes[0,0].invert_yaxis()\n",
    "    \n",
    "    # 2. Change distribution\n",
    "    axes[0,1].hist(changes['change'], bins=30, color='steelblue', alpha=0.7, edgecolor='black')\n",
    "    axes[0,1].axvline(x=0, color='red', linestyle='--', linewidth=2, label='No change')\n",
    "    \n",
    "    if focus_show in changes.index:\n",
    "        focus_change = changes.loc[focus_show, 'change']\n",
    "        axes[0,1].axvline(x=focus_change, color='orange', linestyle='--', linewidth=2, \n",
    "                         label=f'{focus_show}')\n",
    "    \n",
    "    axes[0,1].set_xlabel('Change in Topic Strength')\n",
    "    axes[0,1].set_ylabel('Number of Shows')\n",
    "    axes[0,1].set_title(f'{topic_name} Topic: Distribution of Changes')\n",
    "    axes[0,1].legend()\n",
    "    axes[0,1].grid(True, alpha=0.3, axis='y')\n",
    "    \n",
    "    # 3. Rank changes for top shows\n",
    "    rank_data = []\n",
    "    for show in all_top_shows[:15]:\n",
    "        pre_rank = list(pre_money.sort_values(ascending=False).index).index(show) + 1 if show in pre_money.index else None\n",
    "        post_rank = list(post_money.sort_values(ascending=False).index).index(show) + 1 if show in post_money.index else None\n",
    "        \n",
    "        if pre_rank and post_rank:\n",
    "            rank_data.append({\n",
    "                'show': show,\n",
    "                'pre_rank': pre_rank,\n",
    "                'post_rank': post_rank,\n",
    "                'change': pre_rank - post_rank  # Positive means moved up\n",
    "            })\n",
    "    \n",
    "    rank_df = pd.DataFrame(rank_data).sort_values('post_rank')\n",
    "    \n",
    "    colors = ['red' if show == focus_show else 'steelblue' for show in rank_df['show']]\n",
    "    \n",
    "    for i, row in rank_df.iterrows():\n",
    "        axes[1,0].plot([row['pre_rank'], row['post_rank']], [0, 1], \n",
    "                      marker='o', markersize=8, \n",
    "                      color='red' if row['show'] == focus_show else 'gray',\n",
    "                      alpha=0.8 if row['show'] == focus_show else 0.4,\n",
    "                      linewidth=2 if row['show'] == focus_show else 1)\n",
    "        \n",
    "        # Label\n",
    "        axes[1,0].text(-0.02, 0, f\"#{row['pre_rank']}\", ha='right', va='center', fontsize=8)\n",
    "        axes[1,0].text(1.02, 1, f\"#{row['post_rank']}\", ha='left', va='center', fontsize=8)\n",
    "    \n",
    "    axes[1,0].set_xlim([-0.1, 1.1])\n",
    "    axes[1,0].set_ylim([-0.1, 1.1])\n",
    "    axes[1,0].set_xticks([0, 1])\n",
    "    axes[1,0].set_xticklabels(['Pre-Period', 'Post-Period'])\n",
    "    axes[1,0].set_yticks([])\n",
    "    axes[1,0].set_title(f'{topic_name} Topic: Rank Changes\\n({focus_show} in red)')\n",
    "    axes[1,0].spines['top'].set_visible(False)\n",
    "    axes[1,0].spines['right'].set_visible(False)\n",
    "    axes[1,0].spines['left'].set_visible(False)\n",
    "    \n",
    "    # 4. Focus show trajectory\n",
    "    if focus_show in pre_money.index or focus_show in post_money.index:\n",
    "        pre_val = pre_money.get(focus_show, 0)\n",
    "        post_val = post_money.get(focus_show, 0)\n",
    "        \n",
    "        pre_rank = list(pre_money.sort_values(ascending=False).index).index(focus_show) + 1 if focus_show in pre_money.index else None\n",
    "        post_rank = list(post_money.sort_values(ascending=False).index).index(focus_show) + 1 if focus_show in post_money.index else None\n",
    "        \n",
    "        # Strength trajectory\n",
    "        ax4_1 = axes[1,1]\n",
    "        ax4_2 = ax4_1.twinx()\n",
    "        \n",
    "        periods = ['Pre', 'Post']\n",
    "        strengths = [pre_val, post_val]\n",
    "        ranks = [pre_rank, post_rank] if pre_rank and post_rank else [None, None]\n",
    "        \n",
    "        line1 = ax4_1.plot(periods, strengths, marker='o', markersize=12, \n",
    "                          color='steelblue', linewidth=3, label='Strength')\n",
    "        ax4_1.set_ylabel('Topic Strength', color='steelblue', fontsize=11)\n",
    "        ax4_1.tick_params(axis='y', labelcolor='steelblue')\n",
    "        \n",
    "        if all(ranks):\n",
    "            line2 = ax4_2.plot(periods, ranks, marker='s', markersize=12, \n",
    "                             color='coral', linewidth=3, label='Rank')\n",
    "            ax4_2.set_ylabel('Rank', color='coral', fontsize=11)\n",
    "            ax4_2.tick_params(axis='y', labelcolor='coral')\n",
    "            ax4_2.invert_yaxis()  # Lower rank number = better\n",
    "        \n",
    "        ax4_1.set_title(f\"{focus_show}: {topic_name} Topic Evolution\", fontsize=12, fontweight='bold')\n",
    "        ax4_1.grid(True, alpha=0.3)\n",
    "        \n",
    "        # Add value labels\n",
    "        for i, (p, s) in enumerate(zip(periods, strengths)):\n",
    "            ax4_1.text(i, s, f'{s:.4f}', ha='center', va='bottom', fontsize=10, fontweight='bold')\n",
    "        \n",
    "        if all(ranks):\n",
    "            for i, (p, r) in enumerate(zip(periods, ranks)):\n",
    "                ax4_2.text(i, r, f'#{r}', ha='center', va='top', fontsize=10, fontweight='bold')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'{topic_name.replace(\" \", \"_\")}_evolution_analysis.png', dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# Main function to run the analysis\n",
    "def run_topic_evolution_analysis(pre_df, post_df, pre_topic, post_topic, \n",
    "                                 topic_name=\"Money\", threshold=0.01,\n",
    "                                 focus_show='Squid Game'):\n",
    "    \"\"\"\n",
    "    Run complete topic evolution analysis with visualizations.\n",
    "    \"\"\"\n",
    "    \n",
    "    print(f\"Analyzing {topic_name} topic evolution...\")\n",
    "    print()\n",
    "    \n",
    "    # Run analysis\n",
    "    results = analyze_specific_topic_evolution(\n",
    "        pre_df, post_df, pre_topic, post_topic,\n",
    "        topic_name=topic_name, threshold=threshold, focus_show=focus_show\n",
    "    )\n",
    "    \n",
    "    # Create visualizations\n",
    "    visualize_topic_evolution(results, topic_name=topic_name, focus_show=focus_show)\n",
    "    \n",
    "    return results\n",
    "\n",
    "\n",
    "# Usage:\n",
    "print(\"\\nUsage:\")\n",
    "print(\"money_analysis = run_topic_evolution_analysis(\")\n",
    "print(\"    pre_topics_filtered,\")\n",
    "print(\"    post_topics_filtered,\")\n",
    "print(\"    pre_topic='topic_34',  # Money topic in pre-period\")\n",
    "print(\"    post_topic='topic_18',  # Money topic in post-period\")\n",
    "print(\"    topic_name='Money',\")\n",
    "print(\"    threshold=0.01,\")\n",
    "print(\"    focus_show='Squid Game'\")\n",
    "print(\")\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a9c21c9b-ff45-4393-bfb4-e206bdba1bed",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analyzing Money topic evolution...\n",
      "\n",
      "================================================================================\n",
      "MONEY TOPIC EVOLUTION ANALYSIS\n",
      "================================================================================\n",
      "Pre-period topic:  34\n",
      "Post-period topic: 18\n",
      "Engagement threshold: 0.013\n",
      "\n",
      "================================================================================\n",
      "OVERALL TOPIC METRICS\n",
      "================================================================================\n",
      "Total engagement:  0.4854 → 0.2460 (change: -0.2395)\n",
      "Mean strength:     0.0121 → 0.0117 (change: -0.0004)\n",
      "Shows engaged:     13 → 6 (-7 shows)\n",
      "Engagement rate:   32.5% → 28.6% (change: -3.9%)\n",
      "Fold change:       0.97x\n",
      "\n",
      "================================================================================\n",
      "TOP 15 SHOWS: MONEY TOPIC\n",
      "================================================================================\n",
      "\n",
      "PRE-PERIOD:\n",
      "--------------------------------------------------------------------------------\n",
      "Rank   Show                                     Engagement   % Share\n",
      "--------------------------------------------------------------------------------\n",
      "1      Rick and Morty                           0.0243         5.01%\n",
      "2      Better Call Saul                         0.0189         3.89%\n",
      "3      South Park                               0.0174         3.58%\n",
      "4      Black Mirror                             0.0151         3.10%\n",
      "5      SpongeBob SquarePants                    0.0145         2.98%\n",
      "6      The Boys                                 0.0142         2.93%\n",
      "7      Brooklyn Nine-Nine                       0.0142         2.93%\n",
      "8      The Witcher                              0.0140         2.89%\n",
      "9      The Falcon and the Winter Soldier        0.0139         2.87%\n",
      "10     Westworld                                0.0138         2.85%\n",
      "11     Orange is the New Black                  0.0133         2.75%\n",
      "12     Game of Thrones                          0.0131         2.69%\n",
      "13     Cobra Kai                                0.0128         2.63%\n",
      "14     The Handmaid's Tale                      0.0126         2.60%\n",
      "15     Grey's Anatomy                           0.0126         2.59%\n",
      "\n",
      "POST-PERIOD:\n",
      "--------------------------------------------------------------------------------\n",
      "Rank   Show                                     Engagement   % Share\n",
      "--------------------------------------------------------------------------------\n",
      "1      Squid Game                               0.0206         8.38% ←\n",
      "2      Rick and Morty                           0.0168         6.82%\n",
      "3      Better Call Saul                         0.0159         6.47%\n",
      "4      The Mandalorian                          0.0143         5.81%\n",
      "5      Hawkeye                                  0.0133         5.40%\n",
      "6      The Witcher                              0.0127         5.16%\n",
      "7      The Book of Boba Fett                    0.0127         5.14%\n",
      "8      The Boys                                 0.0114         4.65%\n",
      "9      One Piece                                0.0108         4.38%\n",
      "10     Euphoria                                 0.0107         4.36%\n",
      "11     Cobra Kai                                0.0107         4.36%\n",
      "12     The Wheel of Time                        0.0105         4.25%\n",
      "13     Dragon Ball Super                        0.0104         4.24%\n",
      "14     Moon Knight                              0.0104         4.23%\n",
      "15     The Handmaid's Tale                      0.0103         4.17%\n",
      "\n",
      "================================================================================\n",
      "SQUID GAME ANALYSIS\n",
      "================================================================================\n",
      "Strength:          0.0000 → 0.0206\n",
      "Share of topic:    0.00% → 8.38%\n",
      "Rank:              N/A → 1\n",
      "Status:            NEW - emerged in post-period with 8.38% share\n",
      "\n",
      "================================================================================\n",
      "LEADERSHIP CHANGES\n",
      "================================================================================\n",
      "\n",
      "Maintained top 5:  2 shows\n",
      "  - Better Call Saul: 3.89% → 6.47%\n",
      "  - Rick and Morty: 5.01% → 6.82%\n",
      "\n",
      "Lost top 5:        3 shows\n",
      "  - South Park: 3.58% → 0.00%\n",
      "  - SpongeBob SquarePants: 2.98% → 0.00%\n",
      "  - Black Mirror: 3.10% → 0.00%\n",
      "\n",
      "Gained top 5:      3 shows\n",
      "  - Hawkeye: 0.00% → 5.40%\n",
      "  - The Mandalorian: 2.53% → 5.81%\n",
      "  - Squid Game: 0.00% → 8.38%\n",
      "\n",
      "================================================================================\n",
      "BIGGEST CHANGES\n",
      "================================================================================\n",
      "\n",
      "Top 10 Gainers:\n",
      "--------------------------------------------------------------------------------\n",
      "Rank   Show                                Pre %    Post %   Change\n",
      "--------------------------------------------------------------------------------\n",
      "1      The Mandalorian                     2.53   % 5.81   % +3.27pp\n",
      "2      Better Call Saul                    3.89   % 6.47   % +2.58pp\n",
      "3      The Witcher                         2.89   % 5.16   % +2.28pp\n",
      "4      One Piece                           2.15   % 4.38   % +2.23pp\n",
      "5      Dragon Ball Super                   2.08   % 4.24   % +2.16pp\n",
      "6      Rick and Morty                      5.01   % 6.82   % +1.80pp\n",
      "7      Cobra Kai                           2.63   % 4.36   % +1.73pp\n",
      "8      The Boys                            2.93   % 4.65   % +1.71pp\n",
      "9      Attack on Titan                     1.76   % 3.46   % +1.70pp\n",
      "10     Peaky Blinders                      2.52   % 4.13   % +1.61pp\n",
      "\n",
      "Top 10 Decliners:\n",
      "--------------------------------------------------------------------------------\n",
      "Rank   Show                                Pre %    Post %   Change\n",
      "--------------------------------------------------------------------------------\n",
      "1      The Umbrella Academy                1.83   % 2.83   % +1.00pp\n",
      "2      Game of Thrones                     2.69   % 4.03   % +1.34pp\n",
      "3      Stranger Things                     2.58   % 4.11   % +1.52pp\n",
      "4      The Handmaid's Tale                 2.60   % 4.17   % +1.57pp\n",
      "5      Peaky Blinders                      2.52   % 4.13   % +1.61pp\n",
      "6      Attack on Titan                     1.76   % 3.46   % +1.70pp\n",
      "7      The Boys                            2.93   % 4.65   % +1.71pp\n",
      "8      Cobra Kai                           2.63   % 4.36   % +1.73pp\n",
      "9      Rick and Morty                      5.01   % 6.82   % +1.80pp\n",
      "10     Dragon Ball Super                   2.08   % 4.24   % +2.16pp\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "money_analysis = run_topic_evolution_analysis(\n",
    "    pre_df,\n",
    "    post_df,\n",
    "    pre_topic='34',  # Money topic in pre-period\n",
    "    post_topic='18',  # Money topic in post-period\n",
    "    topic_name='Money',\n",
    "    threshold=0.0127,\n",
    "    focus_show='Squid Game'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "26ede6cd-9453-4a18-a380-4d5e96500f64",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analyzing Choice topic evolution...\n",
      "\n",
      "================================================================================\n",
      "CHOICE TOPIC EVOLUTION ANALYSIS\n",
      "================================================================================\n",
      "Pre-period topic:  46\n",
      "Post-period topic: 73\n",
      "Engagement threshold: 0.013\n",
      "\n",
      "================================================================================\n",
      "OVERALL TOPIC METRICS\n",
      "================================================================================\n",
      "Total engagement:  0.5857 → 0.3292 (change: -0.2565)\n",
      "Mean strength:     0.0146 → 0.0157 (change: +0.0010)\n",
      "Shows engaged:     31 → 20 (-11 shows)\n",
      "Engagement rate:   77.5% → 95.2% (change: +17.7%)\n",
      "Fold change:       1.07x\n",
      "\n",
      "================================================================================\n",
      "TOP 15 SHOWS: CHOICE TOPIC\n",
      "================================================================================\n",
      "\n",
      "PRE-PERIOD:\n",
      "--------------------------------------------------------------------------------\n",
      "Rank   Show                                     Engagement   % Share\n",
      "--------------------------------------------------------------------------------\n",
      "1      The 100                                  0.0242         4.14%\n",
      "2      Attack on Titan                          0.0211         3.60%\n",
      "3      Dark                                     0.0173         2.96%\n",
      "4      Westworld                                0.0172         2.93%\n",
      "5      Vikings                                  0.0170         2.90%\n",
      "6      The Handmaid's Tale                      0.0170         2.90%\n",
      "7      Lucifer                                  0.0168         2.86%\n",
      "8      La Casa De Papel                         0.0163         2.78%\n",
      "9      Loki                                     0.0163         2.78%\n",
      "10     Supernatural                             0.0160         2.73%\n",
      "11     The Falcon and the Winter Soldier        0.0158         2.69%\n",
      "12     One Piece                                0.0158         2.69%\n",
      "13     Orange is the New Black                  0.0157         2.68%\n",
      "14     Game of Thrones                          0.0154         2.64%\n",
      "15     Black Mirror                             0.0151         2.58%\n",
      "\n",
      "POST-PERIOD:\n",
      "--------------------------------------------------------------------------------\n",
      "Rank   Show                                     Engagement   % Share\n",
      "--------------------------------------------------------------------------------\n",
      "1      The Handmaid's Tale                      0.0228         6.94%\n",
      "2      Squid Game                               0.0222         6.75% ←\n",
      "3      Better Call Saul                         0.0197         5.98%\n",
      "4      Attack on Titan                          0.0185         5.61%\n",
      "5      Rick and Morty                           0.0178         5.42%\n",
      "6      The Umbrella Academy                     0.0168         5.10%\n",
      "7      The Boys                                 0.0161         4.89%\n",
      "8      Peaky Blinders                           0.0160         4.87%\n",
      "9      Cobra Kai                                0.0157         4.76%\n",
      "10     Dragon Ball Super                        0.0147         4.48%\n",
      "11     Game of Thrones                          0.0143         4.35%\n",
      "12     The Book of Boba Fett                    0.0143         4.33%\n",
      "13     The Mandalorian                          0.0142         4.31%\n",
      "14     Stranger Things                          0.0141         4.29%\n",
      "15     One Piece                                0.0137         4.17%\n",
      "\n",
      "================================================================================\n",
      "SQUID GAME ANALYSIS\n",
      "================================================================================\n",
      "Strength:          0.0000 → 0.0222\n",
      "Share of topic:    0.00% → 6.75%\n",
      "Rank:              N/A → 2\n",
      "Status:            NEW - emerged in post-period with 6.75% share\n",
      "\n",
      "================================================================================\n",
      "LEADERSHIP CHANGES\n",
      "================================================================================\n",
      "\n",
      "Maintained top 5:  1 shows\n",
      "  - Attack on Titan: 3.60% → 5.61%\n",
      "\n",
      "Lost top 5:        4 shows\n",
      "  - The 100: 4.14% → 0.00%\n",
      "  - Westworld: 2.93% → 0.00%\n",
      "  - Vikings: 2.90% → 0.00%\n",
      "  - Dark: 2.96% → 0.00%\n",
      "\n",
      "Gained top 5:      4 shows\n",
      "  - Better Call Saul: 2.52% → 5.98%\n",
      "  - Squid Game: 0.00% → 6.75%\n",
      "  - Rick and Morty: 2.25% → 5.42%\n",
      "  - The Handmaid's Tale: 2.90% → 6.94%\n",
      "\n",
      "================================================================================\n",
      "BIGGEST CHANGES\n",
      "================================================================================\n",
      "\n",
      "Top 10 Gainers:\n",
      "--------------------------------------------------------------------------------\n",
      "Rank   Show                                Pre %    Post %   Change\n",
      "--------------------------------------------------------------------------------\n",
      "1      The Handmaid's Tale                 2.90   % 6.94   % +4.04pp\n",
      "2      Better Call Saul                    2.52   % 5.98   % +3.46pp\n",
      "3      Rick and Morty                      2.25   % 5.42   % +3.17pp\n",
      "4      The Boys                            1.98   % 4.89   % +2.91pp\n",
      "5      The Umbrella Academy                2.32   % 5.10   % +2.77pp\n",
      "6      Peaky Blinders                      2.37   % 4.87   % +2.50pp\n",
      "7      Cobra Kai                           2.47   % 4.76   % +2.29pp\n",
      "8      Dragon Ball Super                   2.24   % 4.48   % +2.24pp\n",
      "9      The Mandalorian                     2.12   % 4.31   % +2.18pp\n",
      "10     Attack on Titan                     3.60   % 5.61   % +2.00pp\n",
      "\n",
      "Top 10 Decliners:\n",
      "--------------------------------------------------------------------------------\n",
      "Rank   Show                                Pre %    Post %   Change\n",
      "--------------------------------------------------------------------------------\n",
      "1      One Piece                           2.69   % 4.17   % +1.48pp\n",
      "2      Game of Thrones                     2.64   % 4.35   % +1.72pp\n",
      "3      The Witcher                         2.08   % 4.01   % +1.92pp\n",
      "4      Stranger Things                     2.29   % 4.29   % +2.00pp\n",
      "5      Attack on Titan                     3.60   % 5.61   % +2.00pp\n",
      "6      The Mandalorian                     2.12   % 4.31   % +2.18pp\n",
      "7      Dragon Ball Super                   2.24   % 4.48   % +2.24pp\n",
      "8      Cobra Kai                           2.47   % 4.76   % +2.29pp\n",
      "9      Peaky Blinders                      2.37   % 4.87   % +2.50pp\n",
      "10     The Umbrella Academy                2.32   % 5.10   % +2.77pp\n"
     ]
    },
    {
     "data": {
      "image/png": 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PY+zYsTb9oqOjjcjISKNOnTrGrFmzjMWLFxs33nij4eLiYrzyyitGq1atjLlz5xrz5s0zqlevboSFhRkZGRnW7VetWmW4uroabdq0MebMmWMsXbrU6N+/f6Hn60KFnb8bbrjBeOqpp4xly5YZn332mREQEGB06NDhkvvr16+f4eLiYsTExBjjx483fvrpJ2PZsmVGXl6eceuttxpeXl7G2LFjjRUrVhifffaZERERYcTGxtocU79+/QyTyWS88MILxvLly423337biIiIMHx9fY1+/frZjdmlSxejUaNGl8wNAACgpD388MOGJEOS4eXlZfTt29d46623jLVr1xrZ2dl2/deuXWu4urpat8n/iYqKsj4+/3qoXbt21vaCrn8vvCaMjo62tuc7deqU9fr7/J+IiAjr4+jo6IseZ15enuHn52ftP3v27CKfo549e9qNnf/j7e1t7Nq1y9q3X79+1lhISIhd/5tvvtkICgqya3/99det+8jMzLQ5bxf+1KpVyzh16pS1f/718YXnvjBdu3a19n/ooYcu2T//s44kIygoyHB2dr5o/idPnjRq1KhRaP41atSw+QxW0GuelZVldOzYsdB9xMXFGYZhGElJSUZsbGyh/dq2bWtkZWVd8hjznf++vPBcPv/884WO4+LiYsybN6/A/RT0PjCZTDbvwfPfNz///LO1/eWXXy50zHfeeeeix/Lzzz8X+fdj2LBhBf5urFu3ztp+zz33GIZhGBaLxbjjjjsu+jvxxx9/XPTYzn9PXfh598L3w/nv74J+LjzW81+3devWGd7e3oVue8cddxgWi8UuJ19fX8NsNl/0fQ7g+kThDwCAMu6NN94wJBnHjx+3i7366qs2f7CoXLmyMXjwYGPz5s02/W688UajYsWKxtmzZ61tqampRmBgoM0H06sp/J0+fdpwd3c3evfubbPd77//XuAHoQsNHjzYkGT8+++/Nu0Wi8XIycmx/uTm5ha6j7y8PCMnJ8d49dVXjaCgIOuHH8M498HLw8PDiI+Pt7Zt2rTJkGRUqFDBOHPmjLV9/vz5hiRjwYIF1raaNWsaDRs2NHJycmzG7Natm1GhQgUjLy/vosdX2Pl7/PHHbfpNnDjRkGQcO3bsovvL/+D5+eef27TPmjXLkGR8//33Nu0bNmwwJBmTJ082DMMwduzYYUgynnvuOZt+M2bMKPSPLS+99JJhNpuN9PT0i+YGAABQ0rZs2WK4ubkV+EfxoKAg4/XXX7e5NmzTpo01/vDDDxunTp0ytm7dalSrVq3AP7wXR+HvlVdesbY1bdrU2Ldvn3HixAmbgtylChsJCQk2x7Z9+3Zr7NNPP7U79mHDhlnjy5YtMzZu3GgkJiYaOTk5RlJSkk1BZsiQIda+5xc5qlWrZuzcudPYvXu3TfGhdu3axoEDB4zff//dMJlM1mJYvjfffNPad8SIEUZSUpJx5swZ6+cbScbQoUOt/S+38Hd+oeytt966ZP/zCyKSjOeff944deqU9dr/wvwff/xxa3unTp2M+Ph448iRIzaFvPOv5Qt6zd966y1rW3h4uLFo0SIjLS3NOHTokPHBBx8Y+/btMwzDMJ588klrvw8//NBITU01kpOTjaefftqmvagKK/ytX7/e2u7v72+sWrXKSE1NNT744ANre3BwsPXLgufvJygoyFizZo2RkpJijBs3ztoeERFh/YxWUHFs48aN1jaz2Wy8//77RmJiopGYmGjMnTvXWLp06UWP5fxiWEE/5//O7Nmzx/pe7Nq1a4GvZX5e33zzjc0+/vrrLyM5OdmmeFi7dm3rPq628Hep9guP9fzX7fz3+ogRI4zk5GTjr7/+svmiwjfffGOXU1He5wCuTyz1CQBAGXf06FGZTCabZXryjRo1SocOHdLnn3+uQYMGydvbW1OnTlXjxo01a9YsSdKZM2e0YcMG9enTR+7u7tZtfXx81L1792LLc926dcrMzNR9991n096yZUtFR0df8X5/+OEHubi4WH/8/Pxs4qtWrVLHjh3l5+cnJycnubi46JVXXlFSUpISEhJs+jZo0EARERHW57Vq1ZJ0bumm8++Nl9+evwzLnj179O+//1qPLTc31/pz22236dixY9q5c+cVHV+PHj1snterV89m7Eu5/fbbbZ4vWrRI/v7+6t69u02eDRo0UHh4uHXJnZ9//lmS7F6vO++8U87OBd9GOjQ0VBaLRcePHy9SbgAAACWlbt26+vvvv9W3b195e3vbxJKSkjRy5Eh99NFHks7dF/n333+XdG6pvbfeeksBAQGqXbu2hg4des1yXL58ufXxK6+8osqVKys0NFQTJ04s8j6MC5azz8zMLPK24eHhevPNN9WwYUN5eXkpKChI48aNs8a3b99e4HZDhgxR9erVVbVqVcXGxlrbn3nmGUVHR6tly5YKCwuTZHufu3nz5lkfjx8/XkFBQfLy8tKwYcOs7UuXLrU+7t+/v4xzkxyKdJ+488/FhctrXkpISIgmTJiggIAA9ezZU0FBQXb5//DDD9bHkyZNUkREhCpWrKhJkyZZ2xcsWHDRcc4/BxMmTFDXrl3l7e2tqKgoPfnkk6pcubJdvyeffFK+vr7y9/fX+++/b20//1xdqfOPacCAAerQoYN8fHz05JNPqn79+pKkxMRErV271m7bRx99VK1bt5avr69Gjhxp/Vx15MiRQt87kjR//nzr4wcffFBPPfWUgoKCFBQUpN69e6tz585XfVz5qlSpoptuuknSuaVFExISlJOTozlz5kg6t9xn/pKc55+LIUOGqFGjRvLz89O4ceOs74dt27Zp7969xZbfldizZ4/1/AYHB+u1116Tn5+fGjVqpCFDhlj7FfReLMr7HMD1icIfAABl3NmzZ+Xi4lLoTenDwsI0YMAATZ06VVu2bNHq1avl6uqqZ555RpJ0+vRpWSwWhYeH221bUNuVyr9PwpWOU6lSJUn2Ba/27dtrw4YN2rBhg7p162YTW79+vTp16iRJ+vTTT/X7779rw4YNeumllyTJ7r4OgYGBNs9dXV0v2p7/h5QTJ05IkoYOHWpThHRxcdHjjz8u6dwH5CuR/+Ern5ubW4G5F8TT01O+vr42bSdOnFBycrJcXV3tcj1+/Lg1z8JeL2dnZ7uc8uUXjouSGwAAQEmLjY3Vt99+ay1cvP7669ZrTEmaPXu2pP9dH0uSn5+fzfVUUb6wdn7BKTc3t8j5nX+9GBUVdVlj5gsODrbJ9/yCy8MPPyzDMDR69Gi77Q4ePKiWLVtq9uzZOnz4cIH3jCvsGq9q1arWxx4eHtbH+UUr6X/XsFlZWda2/Gvoi7nSa2jpXJEn37Zt2y5r22rVqtl82c3Ly0tS4fmf/xrFxMQU2Kcg539hrm7duoX2u9bnqqBxLnzfXeq4zu9vMpls3sMXfuHyfEU9B0URHR1tLQ7n/1xYxHr00UclnfvdnDVrln788UfrZ5/8mFT4uXB2dlZkZGSB/S7mwqL85fzbcDHnjx8ZGWnzd4FLvWZFeZ8DuD5R+AMAoIwLDg5Wdna2zpw5U6T+bdu2VadOnXTy5EklJCQoICBAJpOpwFlaF7blF3Yu/CCQ/0HpYvKLRUUZpyC33HKLJPtvKvr7+6tJkyZq0qSJXUFq9uzZcnFx0aJFi3TnnXeqZcuWatKkySXHulz5sy1HjBhhLUJe+NOgQYNiH/dSCvpmc3BwsIKCggrNc/LkyZIKf71yc3MLfb1PnTplHQMAAOB6kpKSYn3s5uamFi1aaMSIEZo5c6a1Pf8aJyAgwPrH85SUFKWmplr7FLbqwvkrZ2RkZFgf79mzp8g5nn8Ndfjw4UuOWRCz2WyzasfEiROVl5d3ye3mz59v/Txx00036ciRIzIM45Iz1iQVuhpEYe358mcBSudWB7mwYGMYho4ePXrJ8QvTs2dP6+OZM2fq0KFDBfYrqADj4uJi87yg6+rz8z//NTq/0HR+n4Kc/yW7rVu3Ftovfz8mk0lHjx4t8FwVNAvvchV2TNKlj+v8/oZh2LyHQ0NDCx2zqOeguPTq1UshISGSpK+//lpff/21pHNf7uzXr5+1X2HnIi8vT/Hx8QX2u1Bh/y6kp6cX+hn4cmennj9+fHy8ze/7pV6zorzPAVyfKPwBAFDG1axZU5Lslhg5ceKE9ZvK58vLy9Pu3bvl6ekpf39/eXl5qVmzZpo7d67NUkBpaWlauHChzbZhYWFyd3fXli1bbNrPXwalMM2bN5e7u7tmzJhh07527doi/TGjSZMm6tSpkz799FOtWbPmkv2lcx9cnJ2dbb71ePbsWeuHu+JSo0YNVatWTZs3b7YWIS/88fHxKdYxr1S3bt2UlJSkvLy8AvOsUaOGJFmXuLnw9frmm28K/Xbqvn37FBQUdMk/cAAAAJS0p556Sl26dNF///tfHTx4UDk5OUpISLAp/NWuXVvSuVUTWrVqJelcAWPo0KE6ffq0tm/frjfffLPA/Z8/s2bRokWSpN9//91mGcNLyV+pQpJeffVV7d+/XydPntSLL75Y5H1I0pgxY6zXnlu2bFGPHj20ceNGZWVlKSUlxaZoke/8Ip2rq6u8vLy0d+9em6U+i1vv3r2tj5944gn99ddfysrKUlJSkhYvXqw77rhD48ePt/aZPn26TCaTTCaT+vfvf8n99+vXzzqD7OzZs+rcubOWLVumjIwMZWRkaO3atRo4cKCmTJlyRfmfvyT/sGHDdPToUR07dsxmqdILl+2/UJ8+fayPhw8friVLlig9PV1HjhzRlClTtH//fkn/O1eGYahfv37asWOHcnJydPz4cX333Xe69dZbi+Uzzvn5Tp8+XatXr1Z6eromT56szZs3SzpXoG7ZsqXdtp999pnWrl2rtLQ0vf766zpy5IgkKSIiwmYJ2Av17t3bWnD66quv9NFHHykpKUmnTp3SwoULtWzZsqs+rvOdX+D766+/rMXtPn362BTfzz8X77zzjjZt2qTU1FSNGjXK+iWB2NhYm5mlF8r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      "text/plain": [
       "<Figure size 1800x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "choice_analysis = run_topic_evolution_analysis(\n",
    "    pre_df,\n",
    "    post_df,\n",
    "    pre_topic='46',  # decision-making topic in pre-period\n",
    "    post_topic='73',  # decision-making topic in post-period\n",
    "    topic_name='Choice',\n",
    "    threshold=0.0127,\n",
    "    focus_show='Squid Game'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e7792fa0-3d2a-41a1-8628-4bd6f7be8953",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analyzing Death topic evolution...\n",
      "\n",
      "================================================================================\n",
      "DEATH TOPIC EVOLUTION ANALYSIS\n",
      "================================================================================\n",
      "Pre-period topic:  27\n",
      "Post-period topic: 22\n",
      "Engagement threshold: 0.013\n",
      "\n",
      "================================================================================\n",
      "OVERALL TOPIC METRICS\n",
      "================================================================================\n",
      "Total engagement:  0.6238 → 0.3037 (change: -0.3201)\n",
      "Mean strength:     0.0156 → 0.0145 (change: -0.0011)\n",
      "Shows engaged:     28 → 13 (-15 shows)\n",
      "Engagement rate:   70.0% → 61.9% (change: -8.1%)\n",
      "Fold change:       0.93x\n",
      "\n",
      "================================================================================\n",
      "TOP 15 SHOWS: DEATH TOPIC\n",
      "================================================================================\n",
      "\n",
      "PRE-PERIOD:\n",
      "--------------------------------------------------------------------------------\n",
      "Rank   Show                                     Engagement   % Share\n",
      "--------------------------------------------------------------------------------\n",
      "1      The Walking Dead                         0.0446         7.14%\n",
      "2      The 100                                  0.0259         4.15%\n",
      "3      La Casa De Papel                         0.0254         4.08%\n",
      "4      Vikings                                  0.0210         3.36%\n",
      "5      Game of Thrones                          0.0188         3.01%\n",
      "6      Arrow                                    0.0181         2.90%\n",
      "7      Grey's Anatomy                           0.0179         2.87%\n",
      "8      13 Reasons Why                           0.0177         2.84%\n",
      "9      The Falcon and the Winter Soldier        0.0176         2.83%\n",
      "10     Supernatural                             0.0173         2.78%\n",
      "11     Stranger Things                          0.0171         2.74%\n",
      "12     Better Call Saul                         0.0168         2.70%\n",
      "13     Peaky Blinders                           0.0163         2.61%\n",
      "14     Attack on Titan                          0.0161         2.59%\n",
      "15     Orange is the New Black                  0.0159         2.55%\n",
      "\n",
      "POST-PERIOD:\n",
      "--------------------------------------------------------------------------------\n",
      "Rank   Show                                     Engagement   % Share\n",
      "--------------------------------------------------------------------------------\n",
      "1      Stranger Things                          0.0236         7.78%\n",
      "2      Rick and Morty                           0.0211         6.95%\n",
      "3      Squid Game                               0.0207         6.80% ←\n",
      "4      Peaky Blinders                           0.0188         6.17%\n",
      "5      The Umbrella Academy                     0.0174         5.72%\n",
      "6      Better Call Saul                         0.0170         5.61%\n",
      "7      The Boys                                 0.0157         5.18%\n",
      "8      Hawkeye                                  0.0155         5.11%\n",
      "9      Game of Thrones                          0.0155         5.10%\n",
      "10     The Book of Boba Fett                    0.0154         5.06%\n",
      "11     Dragon Ball Super                        0.0139         4.58%\n",
      "12     One Piece                                0.0135         4.44%\n",
      "13     Attack on Titan                          0.0129         4.26%\n",
      "14     Moon Knight                              0.0122         4.01%\n",
      "15     The Mandalorian                          0.0119         3.93%\n",
      "\n",
      "================================================================================\n",
      "SQUID GAME ANALYSIS\n",
      "================================================================================\n",
      "Strength:          0.0000 → 0.0207\n",
      "Share of topic:    0.00% → 6.80%\n",
      "Rank:              N/A → 3\n",
      "Status:            NEW - emerged in post-period with 6.80% share\n",
      "\n",
      "================================================================================\n",
      "LEADERSHIP CHANGES\n",
      "================================================================================\n",
      "\n",
      "Maintained top 5:  0 shows\n",
      "\n",
      "Lost top 5:        5 shows\n",
      "  - Vikings: 3.36% → 0.00%\n",
      "  - La Casa De Papel: 4.08% → 0.00%\n",
      "  - The 100: 4.15% → 0.00%\n",
      "  - Game of Thrones: 3.01% → 5.10%\n",
      "  - The Walking Dead: 7.14% → 0.00%\n",
      "\n",
      "Gained top 5:      5 shows\n",
      "  - The Umbrella Academy: 2.46% → 5.72%\n",
      "  - Stranger Things: 2.74% → 7.78%\n",
      "  - Squid Game: 0.00% → 6.80%\n",
      "  - Rick and Morty: 2.01% → 6.95%\n",
      "  - Peaky Blinders: 2.61% → 6.17%\n",
      "\n",
      "================================================================================\n",
      "BIGGEST CHANGES\n",
      "================================================================================\n",
      "\n",
      "Top 10 Gainers:\n",
      "--------------------------------------------------------------------------------\n",
      "Rank   Show                                Pre %    Post %   Change\n",
      "--------------------------------------------------------------------------------\n",
      "1      Stranger Things                     2.74   % 7.78   % +5.04pp\n",
      "2      Rick and Morty                      2.01   % 6.95   % +4.93pp\n",
      "3      Peaky Blinders                      2.61   % 6.17   % +3.56pp\n",
      "4      The Umbrella Academy                2.46   % 5.72   % +3.26pp\n",
      "5      Better Call Saul                    2.70   % 5.61   % +2.91pp\n",
      "6      The Boys                            2.36   % 5.18   % +2.82pp\n",
      "7      Dragon Ball Super                   2.04   % 4.58   % +2.54pp\n",
      "8      One Piece                           2.23   % 4.44   % +2.22pp\n",
      "9      Game of Thrones                     3.01   % 5.10   % +2.08pp\n",
      "10     The Handmaid's Tale                 1.96   % 3.82   % +1.86pp\n",
      "\n",
      "Top 10 Decliners:\n",
      "--------------------------------------------------------------------------------\n",
      "Rank   Show                                Pre %    Post %   Change\n",
      "--------------------------------------------------------------------------------\n",
      "1      The Witcher                         1.64   % 3.05   % +1.41pp\n",
      "2      Attack on Titan                     2.59   % 4.26   % +1.67pp\n",
      "3      Cobra Kai                           1.86   % 3.59   % +1.73pp\n",
      "4      The Mandalorian                     2.15   % 3.93   % +1.78pp\n",
      "5      The Handmaid's Tale                 1.96   % 3.82   % +1.86pp\n",
      "6      Game of Thrones                     3.01   % 5.10   % +2.08pp\n",
      "7      One Piece                           2.23   % 4.44   % +2.22pp\n",
      "8      Dragon Ball Super                   2.04   % 4.58   % +2.54pp\n",
      "9      The Boys                            2.36   % 5.18   % +2.82pp\n",
      "10     Better Call Saul                    2.70   % 5.61   % +2.91pp\n"
     ]
    },
    {
     "data": {
      "image/png": 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1unfvLunSU5Hdu3fXihUrnBxh9pgyZYrtuDLGqGfPnra6YcOG2dVNmTIlx+JYunSprZ/reUo1qxo3bmw3lvRPUFBQtvflDL169bKNiSf+AKQj8QcAQB6XnJysSZMm6YEHHrA97SdduoNXUqZP3F3eVpJ+/fVXValSRZ6enipevLjGjh2r4cOH200ts3//flkslgwvDK+ceiSjqT6NMRozZowiIyPl5eWlatWq6bfffsvSONesWaOFCxfq8ccfV6NGjTJsExIS4pDQHDFihGrXrq18+fIpICBA1apV06RJkxz+sFKsWDG1b99e8+bNU9WqVeXt7a1y5cpp3rx5tvGUK1dOvr6+qlWrltauXevQ/9q1a9WxY0fly5dPXl5eqlq1qmbMmJGl8WW2/ZYsWaKnnnpKoaGhCgkJUdeuXXX06NFrrq9Xr17y8/PTf//9p5YtW8rf31/NmjWTdOmYefPNNxUVFSVPT0/lz59fvXv31smTJ+3WkZKSooEDByo8PFw+Pj5q0KCBVq9enWF/HTp0kJ+fn7744ossjRcAAMBZdu7cafu5bdu2Cg0NlZeXl8qWLauHH35Yc+fOVXBwsN0yf/31l+rVqydvb28VLFhQAwcO1G+//ZbhVImZTf+Xfm595fl0ZlM6Hjx4UPfcc48CAgIUEBCgbt266cCBA9c11mHDhikhIUGSVLNmTS1atEiNGzeWj4+PvLy8FBUVpSeffFIrV660e9rv888/V7NmzVS4cGH5+vrKw8NDhQsX1n333efwyoDLp5T86aef1LdvXwUHBys4OFiPPfaYzp49q+3bt6t169by8/NTsWLFNHToUKWkpNitJyUlRePHj1etWrXk7+8vT09PlS1bVoMHD9aZM2fs2l4+/eGNTlPp7e2tmjVrasaMGbbkX0pKigYNGmTXLiYmRi+99JKioqLk7e0tX19f1axZU5999pnDNUW/fv1Uq1YtFShQQJ6envLx8VHZsmXVr18/xcbG2tpZLBbbU16S1Lt37wyPjXQHDx7Ufffdp6CgIOXPn189evRQTEzMDY07I8YYffnll6pfv74CAwPl4eGhyMhI9enTR7t377Zre/n+njt3rp5//nmFh4fLy8tLderU0dKlS+3aX206zOnTp6tFixYKDQ2Vh4eHwsPD1apVK23evDlbxhUbGytPT09ZLBaVLVvWru7ChQsKDAyUxWJRRESEbarb5ORkjR07VtWrV5efn5+8vLxUunRpPf/881maDvbyaWWvPDavnKb3yuvtAwcOOLS52lSf69ev13333adChQrJw8ND+fLlU7NmzTRr1qyrxjR16lRVrlxZ3t7eKl26tN5///0cufEAQA4yAAAgT1u+fLmRZObPn29X/vfffxtJpmbNmmbOnDkmNjY203X88ccfxtXV1TRo0MDMnj3bzJw509SsWdMULVrUXH46sW/fPiPJTJ482WEdksywYcNs3ydPnmwkmX379tnKhg0bZiSZRx991Pz222/m888/N4UKFTLh4eGmcePGVx3nW2+9ZSSZBQsWXLXdlXr16mUmTZpkFi1aZBYtWmTeeOMN4+3tbUaMGGHXLjIy0hQuXNhUrFjRfP/992b+/Pmmdu3axt3d3bz++uumfv36Zvbs2WbOnDmmTJkypkCBAiYxMdG2/J9//mk8PDxMw4YNzQ8//GB+//1306tXr0y315Uy234lSpQwzz33nFmwYIH58ssvTXBwsGnatOk119ezZ0/j7u5uihUrZkaNGmUWL15sFixYYNLS0kzr1q2Nr6+vGTFihFm0aJH58ssvTaFChUz58uXtxtSzZ09jsVjMyy+/bBYuXGjee+89U6hQIRMQEGB69uzp0GebNm1MtWrVrhkbAACAM/Xt29dIMpKMr6+vueeee8y4cePMihUrTHJyskP7FStWGA8PD9sy6Z8iRYrYfr783Khx48a28ozOha88P4yMjLSVpzt16pTtXPzyT6FChWw/R0ZGXnWcaWlpJjAw0NZ++vTpWd5GnTp1cug7/ePn52d27txpa9uzZ09bXf78+R3aN2vWzISEhDiUv/3227Z1XLx40W67XfkpV66cOXXqlK19+rnylds+M0uWLLnqdlu9erWt3mKxmJiYGGOMMXv27DERERGZxnXffffZrefy7X3lp0KFCrbjK7M2lx8blx8X4eHhDu1atmyZ1d3psJ8uv+6wWq2me/fuV93fq1atynA9Ge1vd3d389dff9naZ/b78PDDD2fa55w5c646lsv3/7WuI3v06GFre/k4vv/+e1v5kCFDjDHGXLhwwTRo0CDTuAoUKGB279591bFdfqxdeWxeeQxe/m/ClZ/0NpeP9fL9Nnv2bOPu7p7p8gMGDLC1vTym4ODgDNtPmzbtqtsRwO2FxB8AAHncO++8YySZ48ePO9SNHDnS7o8UxYsXN08++aTZuHGjXbvatWubggULmgsXLtjKzpw5Y/Lly2f3B4ibSfydPn3aeHl5mS5dutgt988//2Tpgu3JJ580ksz27dvtyq1Wq0lJSbF9UlNTM11HWlqaSUlJMSNHjjQhISHGarXa6iIjI423t7c5fPiwrSw6OtpIMhEREeb8+fO28p9++slIMr/88outLCoqylStWtWkpKTY9dm+fXsTERFh0tLSrjq+zLbf008/bdduzJgxRpI5duzYVdeXfkH+1Vdf2ZWnX+DOmjXLrnzNmjVGkvn000+NMcZs27bNSDIvvviiXbupU6dm+geWV155xbi4uJhz585dNTYAAABn2rRpk/H09Mzwj98hISHm7bfftjtPbNiwoa2+b9++5tSpU2bz5s2mdOnSGf6BPzsSf6+//rqtrGbNmmbv3r3mxIkTdgm5ayX+YmJi7Ma2detWW90XX3zhMPZBgwbZ6hcsWGDWrl1rYmNjTUpKiomLizOvvvqqrW3//v1tbS9PBJUuXdrs2LHD7Nq1y/j5+dklvfbv32/++ecfY7FYjCRTtmxZ2zrGjh1rl4SJi4sz58+ft13rXJnIyO7EX2Jiot22WL16tTHm0rm8JOPm5mZmzpxpEhMTzYkTJ+wSZfPmzbOtZ9q0aWb79u0mPj7epKSkmMOHD5vWrVvb2l5+/ZDZ8ZDu8uOiSZMm5tChQ2b79u0mLCzMVn6ta4LLZZb4mzFjht22WbdunYmPjzeDBg2y238ZradkyZJm06ZN5tSpU+app56yldepU8fWPqPfh1mzZtnKfH19zXfffWfi4+PNsWPHzNdff23b/pm5fP9n9Ln82nLx4sW28meeecZW3rZtWyNdSvTu2bPHGPO/ay1JpmrVqmbnzp0mNjbWPPTQQ7bytm3bXnVs15P4u1b5lWNN32+JiYkmNDTUVv7JJ5+YM2fOmD///NMEBAQ4HMeXxyTJvPfeeyYhIcF89NFHtrJWrVpddZsDuL0w1ScAAHnc0aNHZbFY7KbmSffaa6/p4MGD+uqrr/TEE0/Iz89PEydOVPXq1fX9999Lks6fP681a9aoa9eu8vLysi3r7++vDh06ZFucK1eu1MWLF/Xggw/alderV0+RkZE3vN6ff/5Z7u7utk9gYKBd/Z9//qnmzZsrMDBQrq6ucnd31+uvv664uDiH6XGqVKmiQoUK2b6XK1dO0qXpaXx8fBzK06da2r17t7Zv324bW2pqqu3Ttm1bHTt2TDt27Lih8XXs2NHue6VKlez6vpZu3brZfZ83b56CgoLUoUMHuzirVKmi8PBw29Q8S5YskSSH/XXvvffKzc0tw77CwsJktVqzNAUOAACAs9x1111av3697rnnHvn5+dnVxcXFaejQofrkk08kXXpH8j///CPp0jR948aNU3BwsCpUqKABAwbkWIwLFy60/fz666+rePHiCgsL05gxY7K8DnPF1H0XL17M8rLh4eEaO3asqlatKl9fX4WEhOjNN9+01W/dujXD5fr3768yZcqoVKlSKl++vK38hRdeUGRkpOrVq6cCBQpIkt20j3PmzLH9PGrUKIWEhMjX19du2s3ff//d9vPl7z3LiffTWSwWXbx40dZnamqqunfvLh8fHxUoUEAzZ87MMC5vb289++yzKlWqlLy8vFS4cGG7+sy227V88MEHKly4sMqWLauGDRvayq+cOvNG/Pzzz7af+/fvr2rVqikwMFBvvvmmQkJCJElbtmzRnj17HJYdOHCg7rrrLgUHB+vdd9+1XSf8+++/tilmM3L5/n755Zf14IMPKjAwUOHh4XrkkUdUs2bNmx5XuqZNm6p06dKSLk0tmpKSopiYGNvvWPPmzVWiRAlJ9tti+PDhKl26tEJCQvThhx/apuRcuHDhdf0u5YR//vnHNnVs1apV9fTTT8vf319NmzZV7969be1++eUXh2WrVq2qF198UQEBAXZTkWbHsQTg1iHxBwBAHnfhwgW5u7vL1dU1w/oCBQqod+/emjhxojZt2qRly5bJw8NDL7zwgiTp9OnTslqtCg8Pd1g2o7Iblf7OwRvtp2jRopIcE15NmjTRmjVrtGbNGrVv396ubvXq1WrZsqUk6YsvvtA///yjNWvW6JVXXpF0adtdLl++fHbfPTw8rlqefsF34sQJSdKAAQPskpDu7u56+umnJcnunR7XI/1iO52np2eGsWfEx8dHAQEBdmUnTpxQfHy8PDw8HGI9fvy4Lc7M9pebm5tDTOnSE8dZiQ0AAMCZypcvr5kzZyo2NlYrVqzQ22+/bTvflC4lCKT/nStLUmBgoN25VVZuXrs8+Zb+DrGsuPzcsUiRItfVZ7rQ0FC7eC9POvXt21fGGA0bNsxhuQMHDqhevXqaPn26Dh06pOTkZIc2mZ3vlSpVyvazt7e37efixYvbfk4/n01KSrKVpZ9PX82Nnk9nxeXvLUx/v1pcXFyW9ll6XD/++KO6dOmiP/74Q7GxsUpLS3Noe6Pnyek3HkqSr6+v7efsSEBdvu0vP77c3NxUuHDhDNtl1N7X19d2M6ox5qr76/IbBe+6664bC/z/NW7c2JYETv9c/p5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      "text/plain": [
       "<Figure size 1800x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "choice_analysis = run_topic_evolution_analysis(\n",
    "    pre_df,\n",
    "    post_df,\n",
    "    pre_topic='27',  # death/killing topic in pre-period\n",
    "    post_topic='22',  # death/killing topic in post-period\n",
    "    topic_name='Death',\n",
    "    threshold=0.0127,\n",
    "    focus_show='Squid Game'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58bb564f-e180-43ae-a4e8-5a829a9d5d28",
   "metadata": {},
   "source": [
    "### Analysis of Emerging Topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ea64c446-3906-4810-9037-cb7be18a4341",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Emergence Drivers Analysis Ready!\n",
      "\n",
      "Usage:\n",
      "driver_results = run_emergence_drivers_analysis(\n",
      "    comparison,  # From analyze_topic_engagement_rates_with_alignment()\n",
      "    post_topics_filtered,\n",
      "    threshold=0.01,\n",
      "    min_leadership_share=0.05,  # 5% of topic engagement\n",
      "    focus_show='Squid Game'\n",
      ")\n",
      "\n",
      "To see more leaders in network, lower min_leadership_share (e.g., 0.03 for 3%)\n"
     ]
    }
   ],
   "source": [
    "def analyze_emerging_topic_drivers(comparison_df, post_df, threshold=0.01, \n",
    "                                   min_leadership_share=0.05, focus_show='Squid Game'):\n",
    "    \"\"\"\n",
    "    Analyze which shows are driving the emergence of new topics.\n",
    "    Uses alignment results to identify truly emerged topics.\n",
    "    \n",
    "    Parameters:\n",
    "    -----------\n",
    "    comparison_df : DataFrame\n",
    "        Results from analyze_topic_engagement_rates_with_alignment()\n",
    "    post_df : DataFrame\n",
    "        Post-period topic proportions (shows x topics)\n",
    "    threshold : float\n",
    "        Minimum engagement to count as participating\n",
    "    min_leadership_share : float\n",
    "        Minimum share of topic engagement to be considered a \"leader\" (default: 5%)\n",
    "    focus_show : str\n",
    "        Show to highlight in analysis\n",
    "    \"\"\"\n",
    "    \n",
    "    print(\"=\" * 80)\n",
    "    print(\"EMERGING TOPIC DRIVERS ANALYSIS\")\n",
    "    print(\"=\" * 80)\n",
    "    print()\n",
    "    \n",
    "    # Get emerged topics\n",
    "    emerged_topics = comparison_df[comparison_df['status'] == 'emerged']\n",
    "    \n",
    "    if len(emerged_topics) == 0:\n",
    "        print(\"No emerged topics found!\")\n",
    "        return None\n",
    "    \n",
    "    print(f\"Analyzing {len(emerged_topics)} emerged topics...\")\n",
    "    print()\n",
    "    \n",
    "    driver_results = {}\n",
    "    \n",
    "    for idx, row in emerged_topics.iterrows():\n",
    "        post_topic = str(row['post_topic'])\n",
    "        \n",
    "        if post_topic not in post_df.columns:\n",
    "            continue\n",
    "        \n",
    "        topic_engagement = post_df[post_topic]\n",
    "        total_engagement = topic_engagement.sum()\n",
    "        \n",
    "        # Get shows above threshold\n",
    "        engaged_shows = topic_engagement[topic_engagement > threshold].sort_values(ascending=False)\n",
    "        \n",
    "        # Calculate leadership metrics\n",
    "        leaders = {}\n",
    "        for show, engagement in engaged_shows.items():\n",
    "            share = engagement / total_engagement\n",
    "            if share >= min_leadership_share:  # Only shows with significant share\n",
    "                leaders[show] = {\n",
    "                    'engagement': engagement,\n",
    "                    'share': share,\n",
    "                    'rank': list(engaged_shows.index).index(show) + 1\n",
    "                }\n",
    "        \n",
    "        # Calculate concentration metrics\n",
    "        n_engaged = len(engaged_shows)\n",
    "        n_leaders = len(leaders)\n",
    "        herfindahl_index = (topic_engagement**2).sum() / (total_engagement**2) if total_engagement > 0 else 0\n",
    "        \n",
    "        driver_results[idx] = {\n",
    "            'post_topic': post_topic,\n",
    "            'total_engagement': total_engagement,\n",
    "            'n_engaged_shows': n_engaged,\n",
    "            'n_leader_shows': n_leaders,\n",
    "            'leaders': leaders,\n",
    "            'concentration': herfindahl_index,\n",
    "            'top_driver': engaged_shows.index[0] if len(engaged_shows) > 0 else None,\n",
    "            'top_driver_share': engaged_shows.iloc[0] / total_engagement if len(engaged_shows) > 0 else 0,\n",
    "            'focus_show_rank': list(engaged_shows.index).index(focus_show) + 1 if focus_show in engaged_shows.index else None,\n",
    "            'focus_show_share': topic_engagement.get(focus_show, 0) / total_engagement if total_engagement > 0 else 0\n",
    "        }\n",
    "        \n",
    "        # Print details\n",
    "        print(f\"EMERGED TOPIC: {idx} (Post-Topic {post_topic})\")\n",
    "        print(f\"  Total engagement: {total_engagement:.4f}\")\n",
    "        print(f\"  Shows engaged (>{threshold:.3f}): {n_engaged}\")\n",
    "        print(f\"  Leader shows (>{min_leadership_share:.1%} share): {n_leaders}\")\n",
    "        print(f\"  Concentration (Herfindahl): {herfindahl_index:.4f}\")\n",
    "        \n",
    "        # Top drivers\n",
    "        print(f\"  Top 5 drivers:\")\n",
    "        for i, (show, eng) in enumerate(engaged_shows.head(5).items(), 1):\n",
    "            share = eng / total_engagement\n",
    "            is_leader = \"LEADER\" if share >= min_leadership_share else \"participant\"\n",
    "            focus_marker = \" ← FOCUS SHOW\" if show == focus_show else \"\"\n",
    "            print(f\"    {i}. {show}: {eng:.4f} ({share:.1%}) [{is_leader}]{focus_marker}\")\n",
    "        \n",
    "        # Focus show specific\n",
    "        if focus_show in engaged_shows.index:\n",
    "            focus_rank = list(engaged_shows.index).index(focus_show) + 1\n",
    "            focus_eng = engaged_shows[focus_show]\n",
    "            focus_share = focus_eng / total_engagement\n",
    "            print(f\"  → {focus_show}: Rank #{focus_rank}, engagement {focus_eng:.4f} ({focus_share:.1%})\")\n",
    "        else:\n",
    "            print(f\"  → {focus_show}: Not engaged with this topic\")\n",
    "        print()\n",
    "    \n",
    "    return driver_results\n",
    "\n",
    "\n",
    "def analyze_strengthened_topic_drivers(comparison_df, post_df, threshold=0.01,\n",
    "                                       min_leadership_share=0.05, focus_show='Squid Game'):\n",
    "    \"\"\"\n",
    "    Analyze which shows are driving the strengthening of topics.\n",
    "    \"\"\"\n",
    "    \n",
    "    print(\"=\" * 80)\n",
    "    print(\"STRENGTHENED TOPIC DRIVERS ANALYSIS\")\n",
    "    print(\"=\" * 80)\n",
    "    print()\n",
    "    \n",
    "    # Get strengthened topics\n",
    "    strengthened_topics = comparison_df[comparison_df['status'] == 'strengthened']\n",
    "    \n",
    "    if len(strengthened_topics) == 0:\n",
    "        print(\"No strengthened topics found!\")\n",
    "        return None\n",
    "    \n",
    "    print(f\"Analyzing {len(strengthened_topics)} strengthened topics...\")\n",
    "    print()\n",
    "    \n",
    "    driver_results = {}\n",
    "    \n",
    "    for idx, row in strengthened_topics.iterrows():\n",
    "        post_topic = str(row['post_topic'])\n",
    "        \n",
    "        if post_topic not in post_df.columns:\n",
    "            continue\n",
    "        \n",
    "        topic_engagement = post_df[post_topic]\n",
    "        total_engagement = topic_engagement.sum()\n",
    "        \n",
    "        # Get shows above threshold\n",
    "        engaged_shows = topic_engagement[topic_engagement > threshold].sort_values(ascending=False)\n",
    "        \n",
    "        # Calculate leadership metrics\n",
    "        leaders = {}\n",
    "        for show, engagement in engaged_shows.items():\n",
    "            share = engagement / total_engagement\n",
    "            if share >= min_leadership_share:\n",
    "                leaders[show] = {\n",
    "                    'engagement': engagement,\n",
    "                    'share': share,\n",
    "                    'rank': list(engaged_shows.index).index(show) + 1\n",
    "                }\n",
    "        \n",
    "        # Metrics\n",
    "        n_engaged = len(engaged_shows)\n",
    "        n_leaders = len(leaders)\n",
    "        herfindahl_index = (topic_engagement**2).sum() / (total_engagement**2) if total_engagement > 0 else 0\n",
    "        \n",
    "        driver_results[idx] = {\n",
    "            'pre_topic': row['pre_topic'],\n",
    "            'post_topic': post_topic,\n",
    "            'engagement_change': row['engagement_rate_change'],\n",
    "            'total_engagement': total_engagement,\n",
    "            'n_engaged_shows': n_engaged,\n",
    "            'n_leader_shows': n_leaders,\n",
    "            'leaders': leaders,\n",
    "            'concentration': herfindahl_index,\n",
    "            'top_driver': engaged_shows.index[0] if len(engaged_shows) > 0 else None,\n",
    "            'top_driver_share': engaged_shows.iloc[0] / total_engagement if len(engaged_shows) > 0 else 0,\n",
    "            'focus_show_rank': list(engaged_shows.index).index(focus_show) + 1 if focus_show in engaged_shows.index else None,\n",
    "            'focus_show_share': topic_engagement.get(focus_show, 0) / total_engagement if total_engagement > 0 else 0\n",
    "        }\n",
    "        \n",
    "        # Print details\n",
    "        print(f\"STRENGTHENED TOPIC: {idx}\")\n",
    "        print(f\"  Engagement rate change: +{row['engagement_rate_change']:.1%}\")\n",
    "        print(f\"  Total engagement: {total_engagement:.4f}\")\n",
    "        print(f\"  Shows engaged: {n_engaged}\")\n",
    "        print(f\"  Leader shows: {n_leaders}\")\n",
    "        \n",
    "        # Top drivers\n",
    "        print(f\"  Top 5 drivers:\")\n",
    "        for i, (show, eng) in enumerate(engaged_shows.head(5).items(), 1):\n",
    "            share = eng / total_engagement\n",
    "            is_leader = \"LEADER\" if share >= min_leadership_share else \"participant\"\n",
    "            focus_marker = \" ← FOCUS SHOW\" if show == focus_show else \"\"\n",
    "            print(f\"    {i}. {show}: {eng:.4f} ({share:.1%}) [{is_leader}]{focus_marker}\")\n",
    "        \n",
    "        # Focus show specific\n",
    "        if focus_show in engaged_shows.index:\n",
    "            focus_rank = list(engaged_shows.index).index(focus_show) + 1\n",
    "            focus_eng = engaged_shows[focus_show]\n",
    "            focus_share = focus_eng / total_engagement\n",
    "            print(f\"  → {focus_show}: Rank #{focus_rank}, engagement {focus_eng:.4f} ({focus_share:.1%})\")\n",
    "        else:\n",
    "            print(f\"  → {focus_show}: Not engaged with this topic\")\n",
    "        print()\n",
    "    \n",
    "    return driver_results\n",
    "\n",
    "\n",
    "def create_emergence_leadership_network(emerged_drivers, strengthened_drivers, \n",
    "                                        min_leadership_share=0.05):\n",
    "    \"\"\"\n",
    "    Create a bipartite network showing which shows lead which emerging topics.\n",
    "    \"\"\"\n",
    "    \n",
    "    import networkx as nx\n",
    "    \n",
    "    G = nx.Graph()\n",
    "    \n",
    "    # Add emerged topics\n",
    "    if emerged_drivers:\n",
    "        for topic_pair, data in emerged_drivers.items():\n",
    "            topic_node = f\"EMERGED: {topic_pair}\"\n",
    "            G.add_node(topic_node, node_type='topic', status='emerged')\n",
    "            \n",
    "            for show, metrics in data['leaders'].items():\n",
    "                if metrics['share'] >= min_leadership_share:\n",
    "                    G.add_node(show, node_type='show')\n",
    "                    G.add_edge(show, topic_node, weight=metrics['share'], \n",
    "                              engagement=metrics['engagement'])\n",
    "    \n",
    "    # Add strengthened topics\n",
    "    if strengthened_drivers:\n",
    "        for topic_pair, data in strengthened_drivers.items():\n",
    "            topic_node = f\"STRENGTHENED: {topic_pair}\"\n",
    "            G.add_node(topic_node, node_type='topic', status='strengthened')\n",
    "            \n",
    "            for show, metrics in data['leaders'].items():\n",
    "                if metrics['share'] >= min_leadership_share:\n",
    "                    G.add_node(show, node_type='show')\n",
    "                    G.add_edge(show, topic_node, weight=metrics['share'],\n",
    "                              engagement=metrics['engagement'])\n",
    "    \n",
    "    return G\n",
    "\n",
    "\n",
    "def visualize_emergence_drivers(emerged_drivers, strengthened_drivers, G, \n",
    "                                focus_show='Squid Game'):\n",
    "    \"\"\"\n",
    "    Visualize emergence drivers analysis.\n",
    "    \"\"\"\n",
    "    \n",
    "    fig, axes = plt.subplots(2, 2, figsize=(18, 14))\n",
    "    \n",
    "    # 1. Leadership distribution (emerged topics)\n",
    "    if emerged_drivers:\n",
    "        leader_counts = []\n",
    "        topic_labels = []\n",
    "        \n",
    "        for topic_pair, data in emerged_drivers.items():\n",
    "            leader_counts.append(data['n_leader_shows'])\n",
    "            topic_labels.append(f\"Topic {data['post_topic']}\")\n",
    "        \n",
    "        axes[0,0].barh(range(len(leader_counts)), leader_counts, color='darkgreen', alpha=0.7)\n",
    "        axes[0,0].set_yticks(range(len(leader_counts)))\n",
    "        axes[0,0].set_yticklabels(topic_labels, fontsize=9)\n",
    "        axes[0,0].set_xlabel('Number of Leader Shows')\n",
    "        axes[0,0].set_title('Emerged Topics: Leadership Distribution')\n",
    "        axes[0,0].invert_yaxis()\n",
    "        axes[0,0].grid(True, alpha=0.3, axis='x')\n",
    "    \n",
    "    # 2. Focus show's leadership profile\n",
    "    focus_led_emerged = []\n",
    "    focus_led_strengthened = []\n",
    "    \n",
    "    if emerged_drivers:\n",
    "        for topic_pair, data in emerged_drivers.items():\n",
    "            if focus_show in data['leaders']:\n",
    "                focus_led_emerged.append(topic_pair)\n",
    "    \n",
    "    if strengthened_drivers:\n",
    "        for topic_pair, data in strengthened_drivers.items():\n",
    "            if focus_show in data['leaders']:\n",
    "                focus_led_strengthened.append(topic_pair)\n",
    "    \n",
    "    categories = ['Emerged Topics\\n(Leader)', 'Strengthened Topics\\n(Leader)']\n",
    "    counts = [len(focus_led_emerged), len(focus_led_strengthened)]\n",
    "    colors_bar = ['darkgreen', 'green']\n",
    "    \n",
    "    axes[0,1].bar(categories, counts, color=colors_bar, alpha=0.7, edgecolor='black')\n",
    "    axes[0,1].set_ylabel('Number of Topics')\n",
    "    axes[0,1].set_title(f\"{focus_show}'s Leadership Profile\")\n",
    "    axes[0,1].grid(True, alpha=0.3, axis='y')\n",
    "    \n",
    "    for i, v in enumerate(counts):\n",
    "        axes[0,1].text(i, v + 0.1, str(v), ha='center', va='bottom', \n",
    "                      fontsize=12, fontweight='bold')\n",
    "    \n",
    "    # 3. Leadership network\n",
    "    if len(G.nodes()) > 0:\n",
    "        # Separate node types\n",
    "        show_nodes = [n for n, d in G.nodes(data=True) if d.get('node_type') == 'show']\n",
    "        topic_nodes = [n for n, d in G.nodes(data=True) if d.get('node_type') == 'topic']\n",
    "        \n",
    "        # Position nodes\n",
    "        pos = {}\n",
    "        for i, show in enumerate(show_nodes):\n",
    "            pos[show] = (0, i)\n",
    "        for i, topic in enumerate(topic_nodes):\n",
    "            pos[topic] = (2, i)\n",
    "        \n",
    "        # Draw\n",
    "        nx.draw_networkx_nodes(G, pos, nodelist=show_nodes, \n",
    "                              node_color='steelblue', node_size=500, \n",
    "                              ax=axes[1,0], label='Shows')\n",
    "        \n",
    "        emerged_topics = [n for n, d in G.nodes(data=True) \n",
    "                         if d.get('node_type') == 'topic' and d.get('status') == 'emerged']\n",
    "        strengthened_topics = [n for n, d in G.nodes(data=True) \n",
    "                              if d.get('node_type') == 'topic' and d.get('status') == 'strengthened']\n",
    "        \n",
    "        if emerged_topics:\n",
    "            nx.draw_networkx_nodes(G, pos, nodelist=emerged_topics,\n",
    "                                  node_color='darkgreen', node_size=300, \n",
    "                                  ax=axes[1,0], label='Emerged')\n",
    "        if strengthened_topics:\n",
    "            nx.draw_networkx_nodes(G, pos, nodelist=strengthened_topics,\n",
    "                                  node_color='green', node_size=300,\n",
    "                                  ax=axes[1,0], label='Strengthened')\n",
    "        \n",
    "        # Highlight focus show\n",
    "        if focus_show in show_nodes:\n",
    "            nx.draw_networkx_nodes(G, pos, nodelist=[focus_show],\n",
    "                                  node_color='red', node_size=700,\n",
    "                                  ax=axes[1,0])\n",
    "        \n",
    "        # Draw edges\n",
    "        edges = G.edges()\n",
    "        weights = [G[u][v]['weight'] for u, v in edges]\n",
    "        nx.draw_networkx_edges(G, pos, width=[w*5 for w in weights], \n",
    "                              alpha=0.4, ax=axes[1,0])\n",
    "        \n",
    "        # Labels\n",
    "        labels = {n: n.split(': ')[1] if ': ' in n else n for n in G.nodes()}\n",
    "        nx.draw_networkx_labels(G, pos, labels, font_size=7, ax=axes[1,0])\n",
    "        \n",
    "        axes[1,0].set_title('Emergence Leadership Network\\n(Shows → Topics)')\n",
    "        axes[1,0].legend(loc='upper left')\n",
    "        axes[1,0].axis('off')\n",
    "    \n",
    "    # 4. Concentration analysis\n",
    "    if emerged_drivers:\n",
    "        concentrations = [data['concentration'] for data in emerged_drivers.values()]\n",
    "        \n",
    "        axes[1,1].hist(concentrations, bins=15, color='darkgreen', alpha=0.7, \n",
    "                      edgecolor='black')\n",
    "        axes[1,1].axvline(np.mean(concentrations), color='red', linestyle='--',\n",
    "                         linewidth=2, label=f'Mean: {np.mean(concentrations):.3f}')\n",
    "        axes[1,1].set_xlabel('Concentration Index (Herfindahl)')\n",
    "        axes[1,1].set_ylabel('Number of Topics')\n",
    "        axes[1,1].set_title('Emerged Topics: Concentration Distribution\\n(Higher = More Dominated by Few Shows)')\n",
    "        axes[1,1].legend()\n",
    "        axes[1,1].grid(True, alpha=0.3, axis='y')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('emergence_drivers_analysis.png', dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def run_emergence_drivers_analysis(comparison_df, post_df, threshold=0.01,\n",
    "                                   min_leadership_share=0.05, focus_show='Squid Game'):\n",
    "    \"\"\"\n",
    "    Run complete emergence drivers analysis.\n",
    "    \n",
    "    Parameters:\n",
    "    -----------\n",
    "    comparison_df : DataFrame\n",
    "        Results from analyze_topic_engagement_rates_with_alignment()\n",
    "    post_df : DataFrame\n",
    "        Post-period topic proportions (filtered shows x topics)\n",
    "    threshold : float\n",
    "        Minimum engagement to count as participating\n",
    "    min_leadership_share : float\n",
    "        Minimum share to be considered a leader (default: 5%)\n",
    "    focus_show : str\n",
    "        Show to highlight in analysis\n",
    "    \"\"\"\n",
    "    \n",
    "    print(\"\\nStarting emergence drivers analysis...\")\n",
    "    print()\n",
    "    \n",
    "    # Analyze emerged topics\n",
    "    emerged_drivers = analyze_emerging_topic_drivers(\n",
    "        comparison_df, post_df, threshold, min_leadership_share, focus_show\n",
    "    )\n",
    "    \n",
    "    # Analyze strengthened topics\n",
    "    strengthened_drivers = analyze_strengthened_topic_drivers(\n",
    "        comparison_df, post_df, threshold, min_leadership_share, focus_show\n",
    "    )\n",
    "    \n",
    "    # Create leadership network\n",
    "    if emerged_drivers or strengthened_drivers:\n",
    "        G = create_emergence_leadership_network(\n",
    "            emerged_drivers, strengthened_drivers, min_leadership_share\n",
    "        )\n",
    "        \n",
    "        print(f\"\\nLeadership Network Summary:\")\n",
    "        print(f\"  Total leader shows: {len([n for n, d in G.nodes(data=True) if d.get('node_type') == 'show'])}\")\n",
    "        print(f\"  Total emerging topics: {len([n for n, d in G.nodes(data=True) if d.get('node_type') == 'topic'])}\")\n",
    "        print(f\"  Total leadership edges: {len(G.edges())}\")\n",
    "        \n",
    "        # Visualize\n",
    "        visualize_emergence_drivers(emerged_drivers, strengthened_drivers, G, focus_show)\n",
    "    else:\n",
    "        print(\"No drivers found to analyze!\")\n",
    "        G = None\n",
    "    \n",
    "    return {\n",
    "        'emerged_drivers': emerged_drivers,\n",
    "        'strengthened_drivers': strengthened_drivers,\n",
    "        'leadership_network': G\n",
    "    }\n",
    "\n",
    "\n",
    "print(\"\\nEmergence Drivers Analysis Ready!\")\n",
    "print(\"\\nUsage:\")\n",
    "print(\"driver_results = run_emergence_drivers_analysis(\")\n",
    "print(\"    comparison,  # From analyze_topic_engagement_rates_with_alignment()\")\n",
    "print(\"    post_topics_filtered,\")\n",
    "print(\"    threshold=0.01,\")\n",
    "print(\"    min_leadership_share=0.05,  # 5% of topic engagement\")\n",
    "print(\"    focus_show='Squid Game'\")\n",
    "print(\")\")\n",
    "print(\"\\nTo see more leaders in network, lower min_leadership_share (e.g., 0.03 for 3%)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5782de45-4f7a-4afb-9227-f03771786986",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Starting emergence drivers analysis...\n",
      "\n",
      "================================================================================\n",
      "EMERGING TOPIC DRIVERS ANALYSIS\n",
      "================================================================================\n",
      "\n",
      "Analyzing 7 emerged topics...\n",
      "\n",
      "EMERGED TOPIC: NEW->62 (Post-Topic 62)\n",
      "  Total engagement: 0.2623\n",
      "  Shows engaged (>0.005): 21\n",
      "  Leader shows (>5.0% share): 6\n",
      "  Concentration (Herfindahl): 0.0583\n",
      "  Top 5 drivers:\n",
      "    1. Bridgerton: 0.0322 (12.3%) [LEADER]\n",
      "    2. Stranger Things: 0.0248 (9.5%) [LEADER]\n",
      "    3. Euphoria: 0.0177 (6.8%) [LEADER]\n",
      "    4. The Handmaid's Tale: 0.0154 (5.9%) [LEADER]\n",
      "    5. Cobra Kai: 0.0145 (5.5%) [LEADER]\n",
      "  → Squid Game: Rank #17, engagement 0.0085 (3.2%)\n",
      "\n",
      "EMERGED TOPIC: NEW->37 (Post-Topic 37)\n",
      "  Total engagement: 0.2704\n",
      "  Shows engaged (>0.005): 21\n",
      "  Leader shows (>5.0% share): 7\n",
      "  Concentration (Herfindahl): 0.0644\n",
      "  Top 5 drivers:\n",
      "    1. Better Call Saul: 0.0441 (16.3%) [LEADER]\n",
      "    2. Hawkeye: 0.0187 (6.9%) [LEADER]\n",
      "    3. The Handmaid's Tale: 0.0160 (5.9%) [LEADER]\n",
      "    4. Squid Game: 0.0155 (5.7%) [LEADER] ← FOCUS SHOW\n",
      "    5. Cobra Kai: 0.0146 (5.4%) [LEADER]\n",
      "  → Squid Game: Rank #4, engagement 0.0155 (5.7%)\n",
      "\n",
      "EMERGED TOPIC: NEW->11 (Post-Topic 11)\n",
      "  Total engagement: 0.2378\n",
      "  Shows engaged (>0.005): 21\n",
      "  Leader shows (>5.0% share): 5\n",
      "  Concentration (Herfindahl): 0.0513\n",
      "  Top 5 drivers:\n",
      "    1. The Witcher: 0.0202 (8.5%) [LEADER]\n",
      "    2. Bridgerton: 0.0164 (6.9%) [LEADER]\n",
      "    3. The Wheel of Time: 0.0154 (6.5%) [LEADER]\n",
      "    4. Game of Thrones: 0.0151 (6.4%) [LEADER]\n",
      "    5. Euphoria: 0.0146 (6.1%) [LEADER]\n",
      "  → Squid Game: Rank #14, engagement 0.0099 (4.1%)\n",
      "\n",
      "EMERGED TOPIC: NEW->32 (Post-Topic 32)\n",
      "  Total engagement: 0.2053\n",
      "  Shows engaged (>0.005): 21\n",
      "  Leader shows (>5.0% share): 4\n",
      "  Concentration (Herfindahl): 0.0652\n",
      "  Top 5 drivers:\n",
      "    1. Game of Thrones: 0.0277 (13.5%) [LEADER]\n",
      "    2. One Piece: 0.0273 (13.3%) [LEADER]\n",
      "    3. Moon Knight: 0.0112 (5.4%) [LEADER]\n",
      "    4. Dragon Ball Super: 0.0111 (5.4%) [LEADER]\n",
      "    5. The Mandalorian: 0.0096 (4.7%) [participant]\n",
      "  → Squid Game: Rank #18, engagement 0.0066 (3.2%)\n",
      "\n",
      "EMERGED TOPIC: NEW->20 (Post-Topic 20)\n",
      "  Total engagement: 0.2417\n",
      "  Shows engaged (>0.005): 21\n",
      "  Leader shows (>5.0% share): 2\n",
      "  Concentration (Herfindahl): 0.1464\n",
      "  Top 5 drivers:\n",
      "    1. One Piece: 0.0852 (35.3%) [LEADER]\n",
      "    2. Dragon Ball Super: 0.0137 (5.7%) [LEADER]\n",
      "    3. Cobra Kai: 0.0103 (4.2%) [participant]\n",
      "    4. Attack on Titan: 0.0093 (3.9%) [participant]\n",
      "    5. Hawkeye: 0.0090 (3.7%) [participant]\n",
      "  → Squid Game: Rank #12, engagement 0.0073 (3.0%)\n",
      "\n",
      "EMERGED TOPIC: NEW->15 (Post-Topic 15)\n",
      "  Total engagement: 0.2018\n",
      "  Shows engaged (>0.005): 21\n",
      "  Leader shows (>5.0% share): 2\n",
      "  Concentration (Herfindahl): 0.0942\n",
      "  Top 5 drivers:\n",
      "    1. The Wheel of Time: 0.0516 (25.6%) [LEADER]\n",
      "    2. Game of Thrones: 0.0133 (6.6%) [LEADER]\n",
      "    3. The Witcher: 0.0085 (4.2%) [participant]\n",
      "    4. The Mandalorian: 0.0084 (4.2%) [participant]\n",
      "    5. The Book of Boba Fett: 0.0084 (4.2%) [participant]\n",
      "  → Squid Game: Rank #19, engagement 0.0063 (3.1%)\n",
      "\n",
      "EMERGED TOPIC: NEW->66 (Post-Topic 66)\n",
      "  Total engagement: 0.2304\n",
      "  Shows engaged (>0.005): 21\n",
      "  Leader shows (>5.0% share): 2\n",
      "  Concentration (Herfindahl): 0.0837\n",
      "  Top 5 drivers:\n",
      "    1. Squid Game: 0.0533 (23.1%) [LEADER] ← FOCUS SHOW\n",
      "    2. The Witcher: 0.0123 (5.3%) [LEADER]\n",
      "    3. Cobra Kai: 0.0114 (5.0%) [participant]\n",
      "    4. Dragon Ball Super: 0.0104 (4.5%) [participant]\n",
      "    5. Game of Thrones: 0.0101 (4.4%) [participant]\n",
      "  → Squid Game: Rank #1, engagement 0.0533 (23.1%)\n",
      "\n",
      "================================================================================\n",
      "STRENGTHENED TOPIC DRIVERS ANALYSIS\n",
      "================================================================================\n",
      "\n",
      "Analyzing 21 strengthened topics...\n",
      "\n",
      "STRENGTHENED TOPIC: 31->51\n",
      "  Engagement rate change: +90.5%\n",
      "  Total engagement: 0.3547\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 6\n",
      "  Top 5 drivers:\n",
      "    1. Bridgerton: 0.0268 (7.5%) [LEADER]\n",
      "    2. The Witcher: 0.0239 (6.7%) [LEADER]\n",
      "    3. The Wheel of Time: 0.0236 (6.7%) [LEADER]\n",
      "    4. The Book of Boba Fett: 0.0190 (5.3%) [LEADER]\n",
      "    5. Attack on Titan: 0.0182 (5.1%) [LEADER]\n",
      "  → Squid Game: Rank #19, engagement 0.0127 (3.6%)\n",
      "\n",
      "STRENGTHENED TOPIC: 44->64\n",
      "  Engagement rate change: +85.2%\n",
      "  Total engagement: 0.3384\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 5\n",
      "  Top 5 drivers:\n",
      "    1. Attack on Titan: 0.0195 (5.8%) [LEADER]\n",
      "    2. Rick and Morty: 0.0195 (5.8%) [LEADER]\n",
      "    3. Squid Game: 0.0173 (5.1%) [LEADER] ← FOCUS SHOW\n",
      "    4. Moon Knight: 0.0172 (5.1%) [LEADER]\n",
      "    5. Better Call Saul: 0.0170 (5.0%) [LEADER]\n",
      "  → Squid Game: Rank #3, engagement 0.0173 (5.1%)\n",
      "\n",
      "STRENGTHENED TOPIC: 34->25\n",
      "  Engagement rate change: +67.5%\n",
      "  Total engagement: 0.3245\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 5\n",
      "  Top 5 drivers:\n",
      "    1. The Umbrella Academy: 0.0174 (5.4%) [LEADER]\n",
      "    2. Rick and Morty: 0.0173 (5.3%) [LEADER]\n",
      "    3. Moon Knight: 0.0171 (5.3%) [LEADER]\n",
      "    4. Attack on Titan: 0.0168 (5.2%) [LEADER]\n",
      "    5. Euphoria: 0.0163 (5.0%) [LEADER]\n",
      "  → Squid Game: Rank #6, engagement 0.0161 (5.0%)\n",
      "\n",
      "STRENGTHENED TOPIC: 8->16\n",
      "  Engagement rate change: +61.7%\n",
      "  Total engagement: 0.3450\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 6\n",
      "  Top 5 drivers:\n",
      "    1. The Handmaid's Tale: 0.0561 (16.2%) [LEADER]\n",
      "    2. The Umbrella Academy: 0.0211 (6.1%) [LEADER]\n",
      "    3. Peaky Blinders: 0.0198 (5.7%) [LEADER]\n",
      "    4. Game of Thrones: 0.0188 (5.5%) [LEADER]\n",
      "    5. Stranger Things: 0.0179 (5.2%) [LEADER]\n",
      "  → Squid Game: Rank #10, engagement 0.0156 (4.5%)\n",
      "\n",
      "STRENGTHENED TOPIC: 14->72\n",
      "  Engagement rate change: +60.7%\n",
      "  Total engagement: 0.3239\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 5\n",
      "  Top 5 drivers:\n",
      "    1. The Wheel of Time: 0.0246 (7.6%) [LEADER]\n",
      "    2. The Witcher: 0.0231 (7.1%) [LEADER]\n",
      "    3. The Book of Boba Fett: 0.0213 (6.6%) [LEADER]\n",
      "    4. Game of Thrones: 0.0183 (5.6%) [LEADER]\n",
      "    5. Bridgerton: 0.0180 (5.6%) [LEADER]\n",
      "  → Squid Game: Rank #18, engagement 0.0130 (4.0%)\n",
      "\n",
      "STRENGTHENED TOPIC: 28->65\n",
      "  Engagement rate change: +51.9%\n",
      "  Total engagement: 0.3061\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 6\n",
      "  Top 5 drivers:\n",
      "    1. The Mandalorian: 0.0240 (7.9%) [LEADER]\n",
      "    2. Dragon Ball Super: 0.0219 (7.2%) [LEADER]\n",
      "    3. Bridgerton: 0.0186 (6.1%) [LEADER]\n",
      "    4. Moon Knight: 0.0180 (5.9%) [LEADER]\n",
      "    5. The Book of Boba Fett: 0.0167 (5.4%) [LEADER]\n",
      "  → Squid Game: Rank #8, engagement 0.0146 (4.8%)\n",
      "\n",
      "STRENGTHENED TOPIC: 21->65\n",
      "  Engagement rate change: +46.9%\n",
      "  Total engagement: 0.3061\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 6\n",
      "  Top 5 drivers:\n",
      "    1. The Mandalorian: 0.0240 (7.9%) [LEADER]\n",
      "    2. Dragon Ball Super: 0.0219 (7.2%) [LEADER]\n",
      "    3. Bridgerton: 0.0186 (6.1%) [LEADER]\n",
      "    4. Moon Knight: 0.0180 (5.9%) [LEADER]\n",
      "    5. The Book of Boba Fett: 0.0167 (5.4%) [LEADER]\n",
      "  → Squid Game: Rank #8, engagement 0.0146 (4.8%)\n",
      "\n",
      "STRENGTHENED TOPIC: 15->61\n",
      "  Engagement rate change: +44.2%\n",
      "  Total engagement: 0.2947\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 7\n",
      "  Top 5 drivers:\n",
      "    1. The Umbrella Academy: 0.0211 (7.2%) [LEADER]\n",
      "    2. Dragon Ball Super: 0.0192 (6.5%) [LEADER]\n",
      "    3. Rick and Morty: 0.0179 (6.1%) [LEADER]\n",
      "    4. Better Call Saul: 0.0164 (5.6%) [LEADER]\n",
      "    5. Attack on Titan: 0.0150 (5.1%) [LEADER]\n",
      "  → Squid Game: Rank #18, engagement 0.0115 (3.9%)\n",
      "\n",
      "STRENGTHENED TOPIC: 56->57\n",
      "  Engagement rate change: +42.7%\n",
      "  Total engagement: 0.3638\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 8\n",
      "  Top 5 drivers:\n",
      "    1. Cobra Kai: 0.0231 (6.3%) [LEADER]\n",
      "    2. Stranger Things: 0.0228 (6.3%) [LEADER]\n",
      "    3. The Boys: 0.0222 (6.1%) [LEADER]\n",
      "    4. Squid Game: 0.0204 (5.6%) [LEADER] ← FOCUS SHOW\n",
      "    5. Better Call Saul: 0.0202 (5.6%) [LEADER]\n",
      "  → Squid Game: Rank #4, engagement 0.0204 (5.6%)\n",
      "\n",
      "STRENGTHENED TOPIC: 73->8\n",
      "  Engagement rate change: +42.1%\n",
      "  Total engagement: 0.2975\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 7\n",
      "  Top 5 drivers:\n",
      "    1. The Wheel of Time: 0.0246 (8.3%) [LEADER]\n",
      "    2. The Book of Boba Fett: 0.0222 (7.5%) [LEADER]\n",
      "    3. The Witcher: 0.0202 (6.8%) [LEADER]\n",
      "    4. Moon Knight: 0.0169 (5.7%) [LEADER]\n",
      "    5. Game of Thrones: 0.0158 (5.3%) [LEADER]\n",
      "  → Squid Game: Rank #12, engagement 0.0129 (4.3%)\n",
      "\n",
      "STRENGTHENED TOPIC: 14->60\n",
      "  Engagement rate change: +41.7%\n",
      "  Total engagement: 0.2996\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 7\n",
      "  Top 5 drivers:\n",
      "    1. The Handmaid's Tale: 0.0213 (7.1%) [LEADER]\n",
      "    2. The Boys: 0.0175 (5.8%) [LEADER]\n",
      "    3. Better Call Saul: 0.0165 (5.5%) [LEADER]\n",
      "    4. Cobra Kai: 0.0162 (5.4%) [LEADER]\n",
      "    5. Peaky Blinders: 0.0154 (5.1%) [LEADER]\n",
      "  → Squid Game: Rank #17, engagement 0.0122 (4.1%)\n",
      "\n",
      "STRENGTHENED TOPIC: 14->13\n",
      "  Engagement rate change: +36.9%\n",
      "  Total engagement: 0.3229\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 8\n",
      "  Top 5 drivers:\n",
      "    1. The Boys: 0.0230 (7.1%) [LEADER]\n",
      "    2. The Handmaid's Tale: 0.0229 (7.1%) [LEADER]\n",
      "    3. Euphoria: 0.0215 (6.7%) [LEADER]\n",
      "    4. Cobra Kai: 0.0188 (5.8%) [LEADER]\n",
      "    5. The Umbrella Academy: 0.0185 (5.7%) [LEADER]\n",
      "  → Squid Game: Rank #7, engagement 0.0175 (5.4%)\n",
      "\n",
      "STRENGTHENED TOPIC: 74->3\n",
      "  Engagement rate change: +29.6%\n",
      "  Total engagement: 0.3113\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 6\n",
      "  Top 5 drivers:\n",
      "    1. The Boys: 0.0307 (9.9%) [LEADER]\n",
      "    2. The Book of Boba Fett: 0.0253 (8.1%) [LEADER]\n",
      "    3. One Piece: 0.0214 (6.9%) [LEADER]\n",
      "    4. The Mandalorian: 0.0209 (6.7%) [LEADER]\n",
      "    5. Dragon Ball Super: 0.0195 (6.3%) [LEADER]\n",
      "  → Squid Game: Rank #11, engagement 0.0128 (4.1%)\n",
      "\n",
      "STRENGTHENED TOPIC: 57->51\n",
      "  Engagement rate change: +25.5%\n",
      "  Total engagement: 0.3547\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 6\n",
      "  Top 5 drivers:\n",
      "    1. Bridgerton: 0.0268 (7.5%) [LEADER]\n",
      "    2. The Witcher: 0.0239 (6.7%) [LEADER]\n",
      "    3. The Wheel of Time: 0.0236 (6.7%) [LEADER]\n",
      "    4. The Book of Boba Fett: 0.0190 (5.3%) [LEADER]\n",
      "    5. Attack on Titan: 0.0182 (5.1%) [LEADER]\n",
      "  → Squid Game: Rank #19, engagement 0.0127 (3.6%)\n",
      "\n",
      "STRENGTHENED TOPIC: 31->19\n",
      "  Engagement rate change: +23.8%\n",
      "  Total engagement: 0.2268\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 6\n",
      "  Top 5 drivers:\n",
      "    1. One Piece: 0.0145 (6.4%) [LEADER]\n",
      "    2. The Mandalorian: 0.0139 (6.1%) [LEADER]\n",
      "    3. Attack on Titan: 0.0138 (6.1%) [LEADER]\n",
      "    4. Moon Knight: 0.0127 (5.6%) [LEADER]\n",
      "    5. Rick and Morty: 0.0127 (5.6%) [LEADER]\n",
      "  → Squid Game: Rank #8, engagement 0.0110 (4.9%)\n",
      "\n",
      "STRENGTHENED TOPIC: 10->27\n",
      "  Engagement rate change: +18.7%\n",
      "  Total engagement: 0.3373\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 8\n",
      "  Top 5 drivers:\n",
      "    1. Peaky Blinders: 0.0277 (8.2%) [LEADER]\n",
      "    2. Bridgerton: 0.0252 (7.5%) [LEADER]\n",
      "    3. Better Call Saul: 0.0207 (6.1%) [LEADER]\n",
      "    4. Game of Thrones: 0.0203 (6.0%) [LEADER]\n",
      "    5. Stranger Things: 0.0187 (5.5%) [LEADER]\n",
      "  → Squid Game: Rank #16, engagement 0.0133 (3.9%)\n",
      "\n",
      "STRENGTHENED TOPIC: 46->73\n",
      "  Engagement rate change: +17.7%\n",
      "  Total engagement: 0.3292\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 6\n",
      "  Top 5 drivers:\n",
      "    1. The Handmaid's Tale: 0.0228 (6.9%) [LEADER]\n",
      "    2. Squid Game: 0.0222 (6.8%) [LEADER] ← FOCUS SHOW\n",
      "    3. Better Call Saul: 0.0197 (6.0%) [LEADER]\n",
      "    4. Attack on Titan: 0.0185 (5.6%) [LEADER]\n",
      "    5. Rick and Morty: 0.0178 (5.4%) [LEADER]\n",
      "  → Squid Game: Rank #2, engagement 0.0222 (6.8%)\n",
      "\n",
      "STRENGTHENED TOPIC: 66->24\n",
      "  Engagement rate change: +16.0%\n",
      "  Total engagement: 0.3648\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 8\n",
      "  Top 5 drivers:\n",
      "    1. The Umbrella Academy: 0.0297 (8.1%) [LEADER]\n",
      "    2. Bridgerton: 0.0262 (7.2%) [LEADER]\n",
      "    3. Peaky Blinders: 0.0253 (6.9%) [LEADER]\n",
      "    4. Cobra Kai: 0.0238 (6.5%) [LEADER]\n",
      "    5. Stranger Things: 0.0230 (6.3%) [LEADER]\n",
      "  → Squid Game: Rank #19, engagement 0.0119 (3.3%)\n",
      "\n",
      "STRENGTHENED TOPIC: 1->28\n",
      "  Engagement rate change: +15.0%\n",
      "  Total engagement: 0.3656\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 7\n",
      "  Top 5 drivers:\n",
      "    1. Better Call Saul: 0.0207 (5.7%) [LEADER]\n",
      "    2. Peaky Blinders: 0.0206 (5.6%) [LEADER]\n",
      "    3. Game of Thrones: 0.0194 (5.3%) [LEADER]\n",
      "    4. Squid Game: 0.0193 (5.3%) [LEADER] ← FOCUS SHOW\n",
      "    5. Hawkeye: 0.0188 (5.1%) [LEADER]\n",
      "  → Squid Game: Rank #4, engagement 0.0193 (5.3%)\n",
      "\n",
      "STRENGTHENED TOPIC: 1->34\n",
      "  Engagement rate change: +15.0%\n",
      "  Total engagement: 0.3676\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 7\n",
      "  Top 5 drivers:\n",
      "    1. Moon Knight: 0.0225 (6.1%) [LEADER]\n",
      "    2. Hawkeye: 0.0209 (5.7%) [LEADER]\n",
      "    3. The Umbrella Academy: 0.0193 (5.3%) [LEADER]\n",
      "    4. Better Call Saul: 0.0193 (5.3%) [LEADER]\n",
      "    5. The Wheel of Time: 0.0191 (5.2%) [LEADER]\n",
      "  → Squid Game: Rank #7, engagement 0.0186 (5.1%)\n",
      "\n",
      "STRENGTHENED TOPIC: 39->44\n",
      "  Engagement rate change: +13.8%\n",
      "  Total engagement: 0.2744\n",
      "  Shows engaged: 21\n",
      "  Leader shows: 4\n",
      "  Top 5 drivers:\n",
      "    1. The Witcher: 0.0432 (15.8%) [LEADER]\n",
      "    2. The Wheel of Time: 0.0429 (15.6%) [LEADER]\n",
      "    3. Game of Thrones: 0.0195 (7.1%) [LEADER]\n",
      "    4. Bridgerton: 0.0141 (5.1%) [LEADER]\n",
      "    5. Moon Knight: 0.0129 (4.7%) [participant]\n",
      "  → Squid Game: Rank #20, engagement 0.0077 (2.8%)\n",
      "\n",
      "\n",
      "Leadership Network Summary:\n",
      "  Total leader shows: 21\n",
      "  Total emerging topics: 28\n",
      "  Total leadership edges: 162\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "driver_results = run_emergence_drivers_analysis(\n",
    "    comparison,  # From analyze_topic_engagement_rates_with_alignment()\n",
    "    post_df,\n",
    "    threshold=0.005,   #set this to lower rate since we aren't doing classification of engagement; we want to see whole landscape\n",
    "    min_leadership_share=0.05,  # 5% of topic engagement\n",
    "    focus_show='Squid Game'\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
